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Some weights of the model checkpoint at Rostlab/prot_bert were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias']
- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at Rostlab/prot_bert and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/h_ghazik/python_venv/lib/python3.7/site-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
FutureWarning,
***** Running training *****
Num examples = 561
Num Epochs = 5
Instantaneous batch size per device = 1
Total train batch size (w. parallel, distributed & accumulation) = 64
Gradient Accumulation steps = 64
Total optimization steps = 40
Number of trainable parameters = 419933186
Dataset: iontransporters_membraneproteins_balanced_train_4.csv
Model: ProtBERT
--------------------------------------------------
0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:12<08:03, 12.39s/it] 5%|▌ | 2/40 [00:23<07:23, 11.67s/it] 8%|▊ | 3/40 [00:35<07:12, 11.68s/it] 10%|█ | 4/40 [00:47<07:07, 11.88s/it] 12%|█▎ | 5/40 [00:59<06:58, 11.95s/it] 15%|█▌ | 6/40 [01:10<06:37, 11.69s/it] 18%|█▊ | 7/40 [01:22<06:21, 11.57s/it] 20%|██ | 8/40 [01:33<06:12, 11.63s/it] 22%|██▎ | 9/40 [01:54<07:26, 14.41s/it] 25%|██▌ | 10/40 [02:06<06:53, 13.78s/it] 28%|██▊ | 11/40 [02:18<06:21, 13.14s/it] 30%|███ | 12/40 [02:29<05:50, 12.50s/it] 32%|███▎ | 13/40 [02:41<05:32, 12.33s/it] 35%|███▌ | 14/40 [02:52<05:11, 11.97s/it] 38%|███▊ | 15/40 [03:03<04:51, 11.67s/it] 40%|████ | 16/40 [03:15<04:39, 11.64s/it] 42%|████▎ | 17/40 [03:35<05:30, 14.37s/it] 45%|████▌ | 18/40 [03:47<05:01, 13.71s/it] 48%|████▊ | 19/40 [03:59<04:32, 12.96s/it] 50%|█████ | 20/40 [04:10<04:12, 12.63s/it] 52%|█████▎ | 21/40 [04:22<03:52, 12.22s/it] 55%|█████▌ | 22/40 [04:34<03:37, 12.10s/it] 57%|█████▊ | 23/40 [04:45<03:19, 11.76s/it] 60%|██████ | 24/40 [04:56<03:04, 11.56s/it] 62%|██████▎ | 25/40 [05:15<03:30, 14.05s/it] 65%|██████▌ | 26/40 [05:27<03:04, 13.19s/it] 68%|██████▊ | 27/40 [05:39<02:46, 12.79s/it] 70%|███████ | 28/40 [05:49<02:26, 12.23s/it] 72%|███████▎ | 29/40 [06:01<02:12, 12.04s/it] 75%|███████▌ | 30/40 [06:13<01:59, 11.95s/it] 78%|███████▊ | 31/40 [06:24<01:44, 11.58s/it] 80%|████████ | 32/40 [06:36<01:34, 11.75s/it] 82%|████████▎ | 33/40 [06:55<01:39, 14.17s/it] 85%|████████▌ | 34/40 [07:07<01:21, 13.50s/it] 88%|████████▊ | 35/40 [07:19<01:04, 12.86s/it] 90%|█████████ | 36/40 [07:30<00:49, 12.44s/it] 92%|█████████▎| 37/40 [07:42<00:36, 12.22s/it] 95%|█████████▌| 38/40 [07:54<00:24, 12.05s/it] 98%|█████████▊| 39/40 [08:05<00:11, 11.76s/it]100%|██████████| 40/40 [08:16<00:00, 11.60s/it]
Training completed. Do not forget to share your model on huggingface.co/models =)
100%|██████████| 40/40 [08:16<00:00, 11.60s/it]100%|██████████| 40/40 [08:16<00:00, 12.41s/it]
Saving model checkpoint to ./finetuned_models/ProtBERT_full_iontransporters_membraneproteins_balanced_train_4/
Configuration saved in ./finetuned_models/ProtBERT_full_iontransporters_membraneproteins_balanced_train_4/config.json
Model weights saved in ./finetuned_models/ProtBERT_full_iontransporters_membraneproteins_balanced_train_4/pytorch_model.bin
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert_bfd",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
loading file vocab.txt from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/vocab.txt
loading file tokenizer.json from cache at None
loading file added_tokens.json from cache at None
loading file special_tokens_map.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/special_tokens_map.json
loading file tokenizer_config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/tokenizer_config.json
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert_bfd",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert_bfd",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
PyTorch: setting up devices
The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert_bfd",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
loading weights file pytorch_model.bin from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/pytorch_model.bin
Some weights of the model checkpoint at Rostlab/prot_bert_bfd were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias']
- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at Rostlab/prot_bert_bfd and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
***** Running training *****
Num examples = 561
Num Epochs = 5
Instantaneous batch size per device = 1
Total train batch size (w. parallel, distributed & accumulation) = 64
Gradient Accumulation steps = 64
Total optimization steps = 40
Number of trainable parameters = 419933186
{'train_runtime': 496.3943, 'train_samples_per_second': 5.651, 'train_steps_per_second': 0.081, 'train_loss': 0.7455397605895996, 'epoch': 4.91}
Dataset: iontransporters_membraneproteins_balanced_train_4.csv
Model: ProtBERT-BFD
--------------------------------------------------
0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:11<07:25, 11.44s/it] 5%|▌ | 2/40 [00:22<07:10, 11.33s/it] 8%|▊ | 3/40 [00:34<07:06, 11.52s/it] 10%|█ | 4/40 [00:46<07:04, 11.79s/it] 12%|█▎ | 5/40 [00:58<06:57, 11.93s/it] 15%|█▌ | 6/40 [01:10<06:43, 11.87s/it] 18%|█▊ | 7/40 [01:21<06:25, 11.69s/it] 20%|██ | 8/40 [01:33<06:15, 11.73s/it] 22%|██▎ | 9/40 [01:53<07:26, 14.39s/it] 25%|██▌ | 10/40 [02:06<06:55, 13.86s/it] 28%|██▊ | 11/40 [02:18<06:23, 13.22s/it] 30%|███ | 12/40 [02:29<05:52, 12.59s/it] 32%|███▎ | 13/40 [02:41<05:35, 12.42s/it] 35%|███▌ | 14/40 [02:52<05:14, 12.10s/it] 38%|███▊ | 15/40 [03:04<04:54, 11.80s/it] 40%|████ | 16/40 [03:15<04:43, 11.82s/it] 42%|████▎ | 17/40 [03:36<05:34, 14.55s/it] 45%|████▌ | 18/40 [03:48<05:03, 13.82s/it] 48%|████▊ | 19/40 [04:00<04:34, 13.06s/it] 50%|█████ | 20/40 [04:12<04:14, 12.73s/it] 52%|█████▎ | 21/40 [04:23<03:53, 12.30s/it] 55%|█████▌ | 22/40 [04:35<03:39, 12.19s/it] 57%|█████▊ | 23/40 [04:46<03:20, 11.80s/it] 60%|██████ | 24/40 [04:58<03:08, 11.81s/it] 62%|██████▎ | 25/40 [05:18<03:37, 14.47s/it] 65%|██████▌ | 26/40 [05:30<03:09, 13.54s/it] 68%|██████▊ | 27/40 [05:42<02:50, 13.11s/it] 70%|███████ | 28/40 [05:53<02:32, 12.68s/it] 72%|███████▎ | 29/40 [06:05<02:17, 12.49s/it] 75%|███████▌ | 30/40 [06:17<02:03, 12.31s/it] 78%|███████▊ | 31/40 [06:28<01:46, 11.87s/it] 80%|████████ | 32/40 [06:40<01:35, 11.99s/it] 82%|████████▎ | 33/40 [07:01<01:40, 14.42s/it] 85%|████████▌ | 34/40 [07:13<01:22, 13.70s/it] 88%|████████▊ | 35/40 [07:24<01:04, 12.97s/it] 90%|█████████ | 36/40 [07:35<00:49, 12.41s/it] 92%|█████████▎| 37/40 [07:47<00:36, 12.25s/it] 95%|█████████▌| 38/40 [07:59<00:24, 12.10s/it] 98%|█████████▊| 39/40 [08:10<00:11, 11.85s/it]100%|██████████| 40/40 [08:21<00:00, 11.68s/it]
Training completed. Do not forget to share your model on huggingface.co/models =)
100%|██████████| 40/40 [08:21<00:00, 11.68s/it]100%|██████████| 40/40 [08:21<00:00, 12.54s/it]
Saving model checkpoint to ./finetuned_models/ProtBERT-BFD_full_iontransporters_membraneproteins_balanced_train_4/
Configuration saved in ./finetuned_models/ProtBERT-BFD_full_iontransporters_membraneproteins_balanced_train_4/config.json
Model weights saved in ./finetuned_models/ProtBERT-BFD_full_iontransporters_membraneproteins_balanced_train_4/pytorch_model.bin
loading file vocab.txt from cache at /home/h_ghazik/tmp/models--facebook--esm1b_t33_650M_UR50S/snapshots/7b37824baec4d3658e1df7479222a7c79b465b76/vocab.txt
loading file added_tokens.json from cache at None
loading file special_tokens_map.json from cache at /home/h_ghazik/tmp/models--facebook--esm1b_t33_650M_UR50S/snapshots/7b37824baec4d3658e1df7479222a7c79b465b76/special_tokens_map.json
loading file tokenizer_config.json from cache at /home/h_ghazik/tmp/models--facebook--esm1b_t33_650M_UR50S/snapshots/7b37824baec4d3658e1df7479222a7c79b465b76/tokenizer_config.json
PyTorch: setting up devices
The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--facebook--esm1b_t33_650M_UR50S/snapshots/7b37824baec4d3658e1df7479222a7c79b465b76/config.json
Model config EsmConfig {
"_name_or_path": "facebook/esm1b_t33_650M_UR50S",
"architectures": [
"EsmForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"emb_layer_norm_before": true,
"esmfold_config": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1280,
"initializer_range": 0.02,
"intermediate_size": 5120,
"is_folding_model": false,
"layer_norm_eps": 1e-05,
"mask_token_id": 32,
"max_position_embeddings": 1026,
"model_type": "esm",
"num_attention_heads": 20,
"num_hidden_layers": 33,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"token_dropout": true,
"torch_dtype": "float32",
"transformers_version": "4.26.1",
"use_cache": true,
"vocab_list": null,
"vocab_size": 33
}
loading weights file pytorch_model.bin from cache at /home/h_ghazik/tmp/models--facebook--esm1b_t33_650M_UR50S/snapshots/7b37824baec4d3658e1df7479222a7c79b465b76/pytorch_model.bin
Some weights of the model checkpoint at facebook/esm1b_t33_650M_UR50S were not used when initializing EsmForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias']
- This IS expected if you are initializing EsmForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing EsmForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of EsmForSequenceClassification were not initialized from the model checkpoint at facebook/esm1b_t33_650M_UR50S and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
***** Running training *****
Num examples = 561
Num Epochs = 5
Instantaneous batch size per device = 1
Total train batch size (w. parallel, distributed & accumulation) = 64
Gradient Accumulation steps = 64
Total optimization steps = 40
Number of trainable parameters = 652359063
{'train_runtime': 501.6312, 'train_samples_per_second': 5.592, 'train_steps_per_second': 0.08, 'train_loss': 0.7508116722106933, 'epoch': 4.91}
Dataset: iontransporters_membraneproteins_balanced_train_4.csv
Model: ESM-1b
--------------------------------------------------
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Training completed. Do not forget to share your model on huggingface.co/models =)
100%|██████████| 40/40 [12:11<00:00, 17.07s/it]100%|██████████| 40/40 [12:11<00:00, 18.28s/it]
Saving model checkpoint to ./finetuned_models/ESM-1b_full_iontransporters_membraneproteins_balanced_train_4/
Configuration saved in ./finetuned_models/ESM-1b_full_iontransporters_membraneproteins_balanced_train_4/config.json
Model weights saved in ./finetuned_models/ESM-1b_full_iontransporters_membraneproteins_balanced_train_4/pytorch_model.bin
loading file vocab.txt from cache at /home/h_ghazik/tmp/models--facebook--esm2_t33_650M_UR50D/snapshots/08e4846e537177426273712802403f7ba8261b6c/vocab.txt
loading file added_tokens.json from cache at None
loading file special_tokens_map.json from cache at /home/h_ghazik/tmp/models--facebook--esm2_t33_650M_UR50D/snapshots/08e4846e537177426273712802403f7ba8261b6c/special_tokens_map.json
loading file tokenizer_config.json from cache at /home/h_ghazik/tmp/models--facebook--esm2_t33_650M_UR50D/snapshots/08e4846e537177426273712802403f7ba8261b6c/tokenizer_config.json
PyTorch: setting up devices
The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--facebook--esm2_t33_650M_UR50D/snapshots/08e4846e537177426273712802403f7ba8261b6c/config.json
Model config EsmConfig {
"_name_or_path": "facebook/esm2_t33_650M_UR50D",
"architectures": [
"EsmForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"emb_layer_norm_before": false,
"esmfold_config": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1280,
"initializer_range": 0.02,
"intermediate_size": 5120,
"is_folding_model": false,
"layer_norm_eps": 1e-05,
"mask_token_id": 32,
"max_position_embeddings": 1026,
"model_type": "esm",
"num_attention_heads": 20,
"num_hidden_layers": 33,
"pad_token_id": 1,
"position_embedding_type": "rotary",
"token_dropout": true,
"torch_dtype": "float32",
"transformers_version": "4.26.1",
"use_cache": true,
"vocab_list": null,
"vocab_size": 33
}
loading weights file pytorch_model.bin from cache at /home/h_ghazik/tmp/models--facebook--esm2_t33_650M_UR50D/snapshots/08e4846e537177426273712802403f7ba8261b6c/pytorch_model.bin
Some weights of the model checkpoint at facebook/esm2_t33_650M_UR50D were not used when initializing EsmForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias']
- This IS expected if you are initializing EsmForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing EsmForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of EsmForSequenceClassification were not initialized from the model checkpoint at facebook/esm2_t33_650M_UR50D and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
***** Running training *****
Num examples = 561
Num Epochs = 5
Instantaneous batch size per device = 1
Total train batch size (w. parallel, distributed & accumulation) = 64
Gradient Accumulation steps = 64
Total optimization steps = 40
Number of trainable parameters = 652356503
{'train_runtime': 731.3542, 'train_samples_per_second': 3.835, 'train_steps_per_second': 0.055, 'train_loss': 0.7545728206634521, 'epoch': 4.91}
Dataset: iontransporters_membraneproteins_balanced_train_4.csv
Model: ESM-2
--------------------------------------------------
0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:17<11:37, 17.90s/it] 5%|▌ | 2/40 [00:35<11:06, 17.54s/it] 8%|▊ | 3/40 [00:53<10:58, 17.79s/it] 10%|█ | 4/40 [01:12<10:58, 18.30s/it] 12%|█▎ | 5/40 [01:31<10:46, 18.47s/it] 15%|█▌ | 6/40 [01:49<10:28, 18.50s/it] 18%|█▊ | 7/40 [02:07<10:01, 18.24s/it] 20%|██ | 8/40 [02:25<09:45, 18.29s/it] 22%|██▎ | 9/40 [02:57<11:32, 22.35s/it] 25%|██▌ | 10/40 [03:17<10:48, 21.61s/it] 28%|██▊ | 11/40 [03:34<09:52, 20.42s/it] 30%|███ | 12/40 [03:52<09:06, 19.51s/it] 32%|███▎ | 13/40 [04:10<08:39, 19.24s/it] 35%|███▌ | 14/40 [04:27<08:00, 18.49s/it] 38%|███▊ | 15/40 [04:44<07:32, 18.10s/it] 40%|████ | 16/40 [05:03<07:18, 18.26s/it] 42%|████▎ | 17/40 [05:35<08:32, 22.29s/it] 45%|████▌ | 18/40 [05:54<07:49, 21.32s/it] 48%|████▊ | 19/40 [06:11<07:04, 20.22s/it] 50%|█████ | 20/40 [06:30<06:33, 19.67s/it] 52%|█████▎ | 21/40 [06:47<06:01, 19.05s/it] 55%|█████▌ | 22/40 [07:06<05:39, 18.87s/it] 57%|█████▊ | 23/40 [07:23<05:12, 18.37s/it] 60%|██████ | 24/40 [07:41<04:53, 18.35s/it] 62%|██████▎ | 25/40 [08:13<05:36, 22.41s/it] 65%|██████▌ | 26/40 [08:30<04:51, 20.83s/it] 68%|██████▊ | 27/40 [08:49<04:22, 20.21s/it] 70%|███████ | 28/40 [09:06<03:52, 19.38s/it] 72%|███████▎ | 29/40 [09:25<03:30, 19.12s/it] 75%|███████▌ | 30/40 [09:44<03:09, 18.97s/it] 78%|███████▊ | 31/40 [10:00<02:44, 18.32s/it] 80%|████████ | 32/40 [10:19<02:27, 18.48s/it] 82%|████████▎ | 33/40 [10:50<02:35, 22.15s/it] 85%|████████▌ | 34/40 [11:09<02:07, 21.18s/it] 88%|████████▊ | 35/40 [11:27<01:40, 20.20s/it] 90%|█████████ | 36/40 [11:45<01:18, 19.53s/it] 92%|█████████▎| 37/40 [12:03<00:57, 19.28s/it] 95%|█████████▌| 38/40 [12:21<00:37, 18.84s/it] 98%|█████████▊| 39/40 [12:38<00:18, 18.32s/it]100%|██████████| 40/40 [12:56<00:00, 17.99s/it]
Training completed. Do not forget to share your model on huggingface.co/models =)
100%|██████████| 40/40 [12:56<00:00, 17.99s/it]100%|██████████| 40/40 [12:56<00:00, 19.40s/it]
Saving model checkpoint to ./finetuned_models/ESM-2_full_iontransporters_membraneproteins_balanced_train_4/
Configuration saved in ./finetuned_models/ESM-2_full_iontransporters_membraneproteins_balanced_train_4/config.json
Model weights saved in ./finetuned_models/ESM-2_full_iontransporters_membraneproteins_balanced_train_4/pytorch_model.bin
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
loading file vocab.txt from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/vocab.txt
loading file tokenizer.json from cache at None
loading file added_tokens.json from cache at None
loading file special_tokens_map.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/special_tokens_map.json
loading file tokenizer_config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/tokenizer_config.json
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
PyTorch: setting up devices
The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
loading weights file pytorch_model.bin from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/pytorch_model.bin
Some weights of the model checkpoint at Rostlab/prot_bert were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias']
- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at Rostlab/prot_bert and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
***** Running training *****
Num examples = 561
Num Epochs = 5
Instantaneous batch size per device = 1
Total train batch size (w. parallel, distributed & accumulation) = 64
Gradient Accumulation steps = 64
Total optimization steps = 40
Number of trainable parameters = 419933186
{'train_runtime': 776.0914, 'train_samples_per_second': 3.614, 'train_steps_per_second': 0.052, 'train_loss': 0.7799534797668457, 'epoch': 4.91}
Dataset: iontransporters_membraneproteins_balanced_train_10.csv
Model: ProtBERT
--------------------------------------------------
0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:11<07:23, 11.37s/it] 5%|▌ | 2/40 [00:22<07:15, 11.46s/it] 8%|▊ | 3/40 [00:34<07:11, 11.67s/it] 10%|█ | 4/40 [00:46<06:58, 11.61s/it] 12%|█▎ | 5/40 [00:59<07:02, 12.07s/it] 15%|█▌ | 6/40 [01:11<06:48, 12.01s/it] 18%|█▊ | 7/40 [01:22<06:31, 11.85s/it] 20%|██ | 8/40 [01:34<06:23, 11.99s/it] 22%|██▎ | 9/40 [01:54<07:29, 14.49s/it] 25%|██▌ | 10/40 [02:06<06:51, 13.73s/it] 28%|██▊ | 11/40 [02:18<06:18, 13.05s/it] 30%|███ | 12/40 [02:29<05:52, 12.59s/it] 32%|███▎ | 13/40 [02:41<05:33, 12.36s/it] 35%|███▌ | 14/40 [02:54<05:20, 12.34s/it] 38%|███▊ | 15/40 [03:05<04:58, 11.94s/it] 40%|████ | 16/40 [03:17<04:49, 12.05s/it] 42%|████▎ | 17/40 [03:38<05:40, 14.81s/it] 45%|████▌ | 18/40 [03:50<05:03, 13.81s/it] 48%|████▊ | 19/40 [04:01<04:36, 13.18s/it] 50%|█████ | 20/40 [04:13<04:16, 12.81s/it] 52%|█████▎ | 21/40 [04:25<03:57, 12.52s/it] 55%|█████▌ | 22/40 [04:37<03:41, 12.32s/it] 57%|█████▊ | 23/40 [04:49<03:25, 12.07s/it] 60%|██████ | 24/40 [05:00<03:09, 11.83s/it] 62%|██████▎ | 25/40 [05:22<03:42, 14.82s/it] 65%|██████▌ | 26/40 [05:33<03:13, 13.81s/it] 68%|██████▊ | 27/40 [05:44<02:49, 13.01s/it] 70%|███████ | 28/40 [05:56<02:32, 12.73s/it] 72%|███████▎ | 29/40 [06:08<02:16, 12.42s/it] 75%|███████▌ | 30/40 [06:20<02:02, 12.30s/it] 78%|███████▊ | 31/40 [06:31<01:46, 11.84s/it] 80%|████████ | 32/40 [06:42<01:33, 11.75s/it] 82%|████████▎ | 33/40 [07:02<01:39, 14.22s/it] 85%|████████▌ | 34/40 [07:15<01:22, 13.71s/it] 88%|████████▊ | 35/40 [07:26<01:05, 13.09s/it] 90%|█████████ | 36/40 [07:38<00:50, 12.66s/it] 92%|█████████▎| 37/40 [07:50<00:37, 12.35s/it] 95%|█████████▌| 38/40 [08:01<00:23, 11.99s/it] 98%|█████████▊| 39/40 [08:13<00:11, 11.97s/it]100%|██████████| 40/40 [08:24<00:00, 11.82s/it]
Training completed. Do not forget to share your model on huggingface.co/models =)
100%|██████████| 40/40 [08:24<00:00, 11.82s/it]100%|██████████| 40/40 [08:24<00:00, 12.62s/it]
Saving model checkpoint to ./finetuned_models/ProtBERT_full_iontransporters_membraneproteins_balanced_train_10/
Configuration saved in ./finetuned_models/ProtBERT_full_iontransporters_membraneproteins_balanced_train_10/config.json
Model weights saved in ./finetuned_models/ProtBERT_full_iontransporters_membraneproteins_balanced_train_10/pytorch_model.bin
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert_bfd",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
loading file vocab.txt from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/vocab.txt
loading file tokenizer.json from cache at None
loading file added_tokens.json from cache at None
loading file special_tokens_map.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/special_tokens_map.json
loading file tokenizer_config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/tokenizer_config.json
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert_bfd",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert_bfd",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
PyTorch: setting up devices
The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert_bfd",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
loading weights file pytorch_model.bin from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/pytorch_model.bin
Some weights of the model checkpoint at Rostlab/prot_bert_bfd were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias']
- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at Rostlab/prot_bert_bfd and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
***** Running training *****
Num examples = 561
Num Epochs = 5
Instantaneous batch size per device = 1
Total train batch size (w. parallel, distributed & accumulation) = 64
Gradient Accumulation steps = 64
Total optimization steps = 40
Number of trainable parameters = 419933186
{'train_runtime': 504.7102, 'train_samples_per_second': 5.558, 'train_steps_per_second': 0.079, 'train_loss': 0.7459011554718018, 'epoch': 4.91}
Dataset: iontransporters_membraneproteins_balanced_train_10.csv
Model: ProtBERT-BFD
--------------------------------------------------
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Training completed. Do not forget to share your model on huggingface.co/models =)
100%|██████████| 40/40 [08:16<00:00, 11.72s/it]100%|██████████| 40/40 [08:16<00:00, 12.40s/it]
Saving model checkpoint to ./finetuned_models/ProtBERT-BFD_full_iontransporters_membraneproteins_balanced_train_10/
Configuration saved in ./finetuned_models/ProtBERT-BFD_full_iontransporters_membraneproteins_balanced_train_10/config.json
Model weights saved in ./finetuned_models/ProtBERT-BFD_full_iontransporters_membraneproteins_balanced_train_10/pytorch_model.bin
loading file vocab.txt from cache at /home/h_ghazik/tmp/models--facebook--esm1b_t33_650M_UR50S/snapshots/7b37824baec4d3658e1df7479222a7c79b465b76/vocab.txt
loading file added_tokens.json from cache at None
loading file special_tokens_map.json from cache at /home/h_ghazik/tmp/models--facebook--esm1b_t33_650M_UR50S/snapshots/7b37824baec4d3658e1df7479222a7c79b465b76/special_tokens_map.json
loading file tokenizer_config.json from cache at /home/h_ghazik/tmp/models--facebook--esm1b_t33_650M_UR50S/snapshots/7b37824baec4d3658e1df7479222a7c79b465b76/tokenizer_config.json
PyTorch: setting up devices
The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--facebook--esm1b_t33_650M_UR50S/snapshots/7b37824baec4d3658e1df7479222a7c79b465b76/config.json
Model config EsmConfig {
"_name_or_path": "facebook/esm1b_t33_650M_UR50S",
"architectures": [
"EsmForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"emb_layer_norm_before": true,
"esmfold_config": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1280,
"initializer_range": 0.02,
"intermediate_size": 5120,
"is_folding_model": false,
"layer_norm_eps": 1e-05,
"mask_token_id": 32,
"max_position_embeddings": 1026,
"model_type": "esm",
"num_attention_heads": 20,
"num_hidden_layers": 33,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"token_dropout": true,
"torch_dtype": "float32",
"transformers_version": "4.26.1",
"use_cache": true,
"vocab_list": null,
"vocab_size": 33
}
loading weights file pytorch_model.bin from cache at /home/h_ghazik/tmp/models--facebook--esm1b_t33_650M_UR50S/snapshots/7b37824baec4d3658e1df7479222a7c79b465b76/pytorch_model.bin
Some weights of the model checkpoint at facebook/esm1b_t33_650M_UR50S were not used when initializing EsmForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias']
- This IS expected if you are initializing EsmForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing EsmForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of EsmForSequenceClassification were not initialized from the model checkpoint at facebook/esm1b_t33_650M_UR50S and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
***** Running training *****
Num examples = 561
Num Epochs = 5
Instantaneous batch size per device = 1
Total train batch size (w. parallel, distributed & accumulation) = 64
Gradient Accumulation steps = 64
Total optimization steps = 40
Number of trainable parameters = 652359063
{'train_runtime': 496.1879, 'train_samples_per_second': 5.653, 'train_steps_per_second': 0.081, 'train_loss': 0.7504490852355957, 'epoch': 4.91}
Dataset: iontransporters_membraneproteins_balanced_train_10.csv
Model: ESM-1b
--------------------------------------------------
0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:16<10:39, 16.40s/it] 5%|▌ | 2/40 [00:32<10:27, 16.50s/it] 8%|▊ | 3/40 [00:50<10:25, 16.91s/it] 10%|█ | 4/40 [01:07<10:10, 16.96s/it] 12%|█▎ | 5/40 [01:26<10:22, 17.78s/it] 15%|█▌ | 6/40 [01:44<10:00, 17.66s/it] 18%|█▊ | 7/40 [02:00<09:33, 17.38s/it] 20%|██ | 8/40 [02:17<09:12, 17.27s/it] 22%|██▎ | 9/40 [02:46<10:44, 20.80s/it] 25%|██▌ | 10/40 [03:04<09:54, 19.82s/it] 28%|██▊ | 11/40 [03:20<09:03, 18.73s/it] 30%|███ | 12/40 [03:37<08:27, 18.13s/it] 32%|███▎ | 13/40 [03:54<08:03, 17.91s/it] 35%|███▌ | 14/40 [04:12<07:49, 18.04s/it] 38%|███▊ | 15/40 [04:29<07:18, 17.53s/it] 40%|████ | 16/40 [04:47<07:05, 17.75s/it] 42%|████▎ | 17/40 [05:18<08:21, 21.81s/it] 45%|████▌ | 18/40 [05:35<07:26, 20.30s/it] 48%|████▊ | 19/40 [05:52<06:45, 19.32s/it] 50%|█████ | 20/40 [06:09<06:13, 18.70s/it] 52%|█████▎ | 21/40 [06:27<05:47, 18.31s/it] 55%|█████▌ | 22/40 [06:45<05:27, 18.21s/it] 57%|█████▊ | 23/40 [07:02<05:03, 17.85s/it] 60%|██████ | 24/40 [07:18<04:36, 17.30s/it] 62%|██████▎ | 25/40 [07:50<05:25, 21.67s/it] 65%|██████▌ | 26/40 [08:07<04:44, 20.36s/it] 68%|██████▊ | 27/40 [08:23<04:07, 19.05s/it] 70%|███████ | 28/40 [08:41<03:43, 18.65s/it] 72%|███████▎ | 29/40 [08:58<03:20, 18.27s/it] 75%|███████▌ | 30/40 [09:16<03:01, 18.11s/it] 78%|███████▊ | 31/40 [09:32<02:37, 17.49s/it] 80%|████████ | 32/40 [09:49<02:20, 17.52s/it] 82%|████████▎ | 33/40 [10:18<02:26, 20.98s/it] 85%|████████▌ | 34/40 [10:37<02:02, 20.38s/it] 88%|████████▊ | 35/40 [10:54<01:36, 19.31s/it] 90%|█████████ | 36/40 [11:11<01:14, 18.55s/it] 92%|█████████▎| 37/40 [11:28<00:54, 18.12s/it] 95%|█████████▌| 38/40 [11:44<00:35, 17.53s/it] 98%|█████████▊| 39/40 [12:02<00:17, 17.47s/it]100%|██████████| 40/40 [12:18<00:00, 17.16s/it]
Training completed. Do not forget to share your model on huggingface.co/models =)
100%|██████████| 40/40 [12:18<00:00, 17.16s/it]100%|██████████| 40/40 [12:18<00:00, 18.46s/it]
Saving model checkpoint to ./finetuned_models/ESM-1b_full_iontransporters_membraneproteins_balanced_train_10/
Configuration saved in ./finetuned_models/ESM-1b_full_iontransporters_membraneproteins_balanced_train_10/config.json
Model weights saved in ./finetuned_models/ESM-1b_full_iontransporters_membraneproteins_balanced_train_10/pytorch_model.bin
loading file vocab.txt from cache at /home/h_ghazik/tmp/models--facebook--esm2_t33_650M_UR50D/snapshots/08e4846e537177426273712802403f7ba8261b6c/vocab.txt
loading file added_tokens.json from cache at None
loading file special_tokens_map.json from cache at /home/h_ghazik/tmp/models--facebook--esm2_t33_650M_UR50D/snapshots/08e4846e537177426273712802403f7ba8261b6c/special_tokens_map.json
loading file tokenizer_config.json from cache at /home/h_ghazik/tmp/models--facebook--esm2_t33_650M_UR50D/snapshots/08e4846e537177426273712802403f7ba8261b6c/tokenizer_config.json
PyTorch: setting up devices
The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--facebook--esm2_t33_650M_UR50D/snapshots/08e4846e537177426273712802403f7ba8261b6c/config.json
Model config EsmConfig {
"_name_or_path": "facebook/esm2_t33_650M_UR50D",
"architectures": [
"EsmForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"emb_layer_norm_before": false,
"esmfold_config": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1280,
"initializer_range": 0.02,
"intermediate_size": 5120,
"is_folding_model": false,
"layer_norm_eps": 1e-05,
"mask_token_id": 32,
"max_position_embeddings": 1026,
"model_type": "esm",
"num_attention_heads": 20,
"num_hidden_layers": 33,
"pad_token_id": 1,
"position_embedding_type": "rotary",
"token_dropout": true,
"torch_dtype": "float32",
"transformers_version": "4.26.1",
"use_cache": true,
"vocab_list": null,
"vocab_size": 33
}
loading weights file pytorch_model.bin from cache at /home/h_ghazik/tmp/models--facebook--esm2_t33_650M_UR50D/snapshots/08e4846e537177426273712802403f7ba8261b6c/pytorch_model.bin
Some weights of the model checkpoint at facebook/esm2_t33_650M_UR50D were not used when initializing EsmForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias']
- This IS expected if you are initializing EsmForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing EsmForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of EsmForSequenceClassification were not initialized from the model checkpoint at facebook/esm2_t33_650M_UR50D and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
***** Running training *****
Num examples = 561
Num Epochs = 5
Instantaneous batch size per device = 1
Total train batch size (w. parallel, distributed & accumulation) = 64
Gradient Accumulation steps = 64
Total optimization steps = 40
Number of trainable parameters = 652356503
{'train_runtime': 738.4891, 'train_samples_per_second': 3.798, 'train_steps_per_second': 0.054, 'train_loss': 0.7543065071105957, 'epoch': 4.91}
Dataset: iontransporters_membraneproteins_balanced_train_10.csv
Model: ESM-2
--------------------------------------------------
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Training completed. Do not forget to share your model on huggingface.co/models =)
100%|██████████| 40/40 [13:03<00:00, 18.43s/it]100%|██████████| 40/40 [13:03<00:00, 19.59s/it]
Saving model checkpoint to ./finetuned_models/ESM-2_full_iontransporters_membraneproteins_balanced_train_10/
Configuration saved in ./finetuned_models/ESM-2_full_iontransporters_membraneproteins_balanced_train_10/config.json
Model weights saved in ./finetuned_models/ESM-2_full_iontransporters_membraneproteins_balanced_train_10/pytorch_model.bin
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
loading file vocab.txt from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/vocab.txt
loading file tokenizer.json from cache at None
loading file added_tokens.json from cache at None
loading file special_tokens_map.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/special_tokens_map.json
loading file tokenizer_config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/tokenizer_config.json
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
PyTorch: setting up devices
The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
loading weights file pytorch_model.bin from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert/snapshots/3d05bf06e79014892defacad82e0efd06e977ff6/pytorch_model.bin
Some weights of the model checkpoint at Rostlab/prot_bert were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias']
- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at Rostlab/prot_bert and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
***** Running training *****
Num examples = 522
Num Epochs = 5
Instantaneous batch size per device = 1
Total train batch size (w. parallel, distributed & accumulation) = 64
Gradient Accumulation steps = 64
Total optimization steps = 40
Number of trainable parameters = 419933186
{'train_runtime': 783.6939, 'train_samples_per_second': 3.579, 'train_steps_per_second': 0.051, 'train_loss': 0.7796895980834961, 'epoch': 4.91}
Dataset: ionchannels_iontransporters_train.csv
Model: ProtBERT
--------------------------------------------------
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Training completed. Do not forget to share your model on huggingface.co/models =)
100%|██████████| 40/40 [08:31<00:00, 12.75s/it]100%|██████████| 40/40 [08:31<00:00, 12.79s/it]
Saving model checkpoint to ./finetuned_models/ProtBERT_full_ionchannels_iontransporters_train/
Configuration saved in ./finetuned_models/ProtBERT_full_ionchannels_iontransporters_train/config.json
Model weights saved in ./finetuned_models/ProtBERT_full_ionchannels_iontransporters_train/pytorch_model.bin
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert_bfd",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
loading file vocab.txt from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/vocab.txt
loading file tokenizer.json from cache at None
loading file added_tokens.json from cache at None
loading file special_tokens_map.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/special_tokens_map.json
loading file tokenizer_config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/tokenizer_config.json
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert_bfd",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert_bfd",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
PyTorch: setting up devices
The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/config.json
Model config BertConfig {
"_name_or_path": "Rostlab/prot_bert_bfd",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 40000,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 30,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.26.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30
}
loading weights file pytorch_model.bin from cache at /home/h_ghazik/tmp/models--Rostlab--prot_bert_bfd/snapshots/6c5c8a55a52ff08a664dfd584aa1773f125a0487/pytorch_model.bin
Some weights of the model checkpoint at Rostlab/prot_bert_bfd were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias']
- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at Rostlab/prot_bert_bfd and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
***** Running training *****
Num examples = 522
Num Epochs = 5
Instantaneous batch size per device = 1
Total train batch size (w. parallel, distributed & accumulation) = 64
Gradient Accumulation steps = 64
Total optimization steps = 40
Number of trainable parameters = 419933186
{'train_runtime': 511.5163, 'train_samples_per_second': 5.102, 'train_steps_per_second': 0.078, 'train_loss': 0.6997018814086914, 'epoch': 4.98}
Dataset: ionchannels_iontransporters_train.csv
Model: ProtBERT-BFD
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Training completed. Do not forget to share your model on huggingface.co/models =)
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Saving model checkpoint to ./finetuned_models/ProtBERT-BFD_full_ionchannels_iontransporters_train/
Configuration saved in ./finetuned_models/ProtBERT-BFD_full_ionchannels_iontransporters_train/config.json
Model weights saved in ./finetuned_models/ProtBERT-BFD_full_ionchannels_iontransporters_train/pytorch_model.bin
loading file vocab.txt from cache at /home/h_ghazik/tmp/models--facebook--esm1b_t33_650M_UR50S/snapshots/7b37824baec4d3658e1df7479222a7c79b465b76/vocab.txt
loading file added_tokens.json from cache at None
loading file special_tokens_map.json from cache at /home/h_ghazik/tmp/models--facebook--esm1b_t33_650M_UR50S/snapshots/7b37824baec4d3658e1df7479222a7c79b465b76/special_tokens_map.json
loading file tokenizer_config.json from cache at /home/h_ghazik/tmp/models--facebook--esm1b_t33_650M_UR50S/snapshots/7b37824baec4d3658e1df7479222a7c79b465b76/tokenizer_config.json
PyTorch: setting up devices
The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
loading configuration file config.json from cache at /home/h_ghazik/tmp/models--facebook--esm1b_t33_650M_UR50S/snapshots/7b37824baec4d3658e1df7479222a7c79b465b76/config.json
Model config EsmConfig {
"_name_or_path": "facebook/esm1b_t33_650M_UR50S",
"architectures": [
"EsmForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"emb_layer_norm_before": true,
"esmfold_config": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1280,
"initializer_range": 0.02,
"intermediate_size": 5120,
"is_folding_model": false,
"layer_norm_eps": 1e-05,