Training complete

README.md

@@ -1,74 +1,75 @@
----
-license: mit
-base_model: emilyalsentzer/Bio_ClinicalBERT
-tags:
-- generated_from_trainer
-metrics:
-- precision
-- recall
-- f1
-- accuracy
-model-index:
-- name: BioClinicalBERT-full-finetuned-ner-pablo
-  results: []
----
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# BioClinicalBERT-full-finetuned-ner-pablo
-
-This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1136
-- Precision: 0.8112
-- Recall: 0.8083
-- F1: 0.8098
-- Accuracy: 0.9747
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.0002
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 64
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.05
-- num_epochs: 5
-- mixed_precision_training: Native AMP
-
-### Training results
-
-| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.2583        | 0.9990 | 781  | 0.0983          | 0.7777    | 0.7768 | 0.7773 | 0.9721   |
-| 0.0794        | 1.9994 | 1563 | 0.0944          | 0.7819    | 0.7879 | 0.7849 | 0.9736   |
-| 0.0614        | 2.9997 | 2345 | 0.0913          | 0.7861    | 0.8018 | 0.7939 | 0.9733   |
-| 0.0408        | 4.0    | 3127 | 0.1031          | 0.8007    | 0.8006 | 0.8006 | 0.9736   |
-| 0.0298        | 4.9952 | 3905 | 0.1136          | 0.8112    | 0.8083 | 0.8098 | 0.9747   |
-
-
-### Framework versions
-
-- Transformers 4.44.0
-- Pytorch 2.4.0+cu124
-- Datasets 2.21.0
-- Tokenizers 0.19.1
+---
+library_name: transformers
+license: mit
+base_model: emilyalsentzer/Bio_ClinicalBERT
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: BioClinicalBERT-full-finetuned-ner-pablo
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# BioClinicalBERT-full-finetuned-ner-pablo
+
+This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1111
+- Precision: 0.8031
+- Recall: 0.7991
+- F1: 0.8011
+- Accuracy: 0.9748
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.05
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 0.9970 | 252  | 0.0965          | 0.7524    | 0.7852 | 0.7684 | 0.9711   |
+| 0.1751        | 1.9980 | 505  | 0.0858          | 0.7924    | 0.7861 | 0.7892 | 0.9747   |
+| 0.1751        | 2.9990 | 758  | 0.0870          | 0.7932    | 0.7944 | 0.7938 | 0.9746   |
+| 0.0409        | 4.0    | 1011 | 0.0970          | 0.8050    | 0.8026 | 0.8038 | 0.9749   |
+| 0.0409        | 4.9852 | 1260 | 0.1111          | 0.8031    | 0.7991 | 0.8011 | 0.9748   |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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