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NER-finetuning-BETO-CM-V3

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End of training

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  1. README.md +23 -23
  2. model.safetensors +1 -1
  3. runs/Nov20_13-01-38_bfa7ddf21995/events.out.tfevents.1732107720.bfa7ddf21995.744.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -26,16 +26,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9326628771316134
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  - name: Recall
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  type: recall
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- value: 0.9424500165690931
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  - name: F1
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  type: f1
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- value: 0.9375309048953355
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  - name: Accuracy
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  type: accuracy
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- value: 0.977440285204991
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -45,11 +45,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on the biobert_json dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1452
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- - Precision: 0.9327
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- - Recall: 0.9425
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- - F1: 0.9375
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- - Accuracy: 0.9774
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  ## Model description
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@@ -69,8 +69,8 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 8
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- - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
@@ -78,18 +78,18 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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- |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.0459 | 1.0 | 1224 | 0.1312 | 0.9209 | 0.9487 | 0.9346 | 0.9721 |
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- | 0.0457 | 2.0 | 2448 | 0.1208 | 0.9199 | 0.9584 | 0.9388 | 0.9761 |
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- | 0.0473 | 3.0 | 3672 | 0.1092 | 0.9341 | 0.9527 | 0.9433 | 0.9797 |
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- | 0.0335 | 4.0 | 4896 | 0.1123 | 0.9321 | 0.9482 | 0.9401 | 0.9774 |
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- | 0.0299 | 5.0 | 6120 | 0.1190 | 0.9313 | 0.9475 | 0.9393 | 0.9768 |
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- | 0.0273 | 6.0 | 7344 | 0.1224 | 0.9335 | 0.9444 | 0.9389 | 0.9774 |
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- | 0.0208 | 7.0 | 8568 | 0.1354 | 0.9356 | 0.9463 | 0.9409 | 0.9782 |
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- | 0.02 | 8.0 | 9792 | 0.1371 | 0.9302 | 0.9434 | 0.9368 | 0.9774 |
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- | 0.0158 | 9.0 | 11016 | 0.1408 | 0.9320 | 0.9448 | 0.9383 | 0.9777 |
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- | 0.0153 | 10.0 | 12240 | 0.1452 | 0.9327 | 0.9425 | 0.9375 | 0.9774 |
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9332171260485892
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  - name: Recall
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  type: recall
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+ value: 0.9462056776759086
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  - name: F1
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  type: f1
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+ value: 0.9396665204036859
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9769126559714795
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on the biobert_json dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1234
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+ - Precision: 0.9332
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+ - Recall: 0.9462
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+ - F1: 0.9397
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+ - Accuracy: 0.9769
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
 
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.3448 | 1.0 | 612 | 0.1106 | 0.9187 | 0.9255 | 0.9221 | 0.9719 |
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+ | 0.1036 | 2.0 | 1224 | 0.0990 | 0.9202 | 0.9507 | 0.9352 | 0.9763 |
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+ | 0.073 | 3.0 | 1836 | 0.0982 | 0.9356 | 0.9493 | 0.9424 | 0.9783 |
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+ | 0.057 | 4.0 | 2448 | 0.1070 | 0.9304 | 0.9493 | 0.9397 | 0.9771 |
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+ | 0.0405 | 5.0 | 3060 | 0.1034 | 0.9353 | 0.9486 | 0.9419 | 0.9783 |
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+ | 0.0361 | 6.0 | 3672 | 0.1081 | 0.9280 | 0.9474 | 0.9376 | 0.9767 |
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+ | 0.0287 | 7.0 | 4284 | 0.1106 | 0.9309 | 0.9490 | 0.9398 | 0.9777 |
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+ | 0.0284 | 8.0 | 4896 | 0.1182 | 0.9288 | 0.9463 | 0.9375 | 0.9768 |
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+ | 0.0212 | 9.0 | 5508 | 0.1195 | 0.9340 | 0.9464 | 0.9402 | 0.9774 |
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+ | 0.0191 | 10.0 | 6120 | 0.1234 | 0.9332 | 0.9462 | 0.9397 | 0.9769 |
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  ### Framework versions
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