Training complete

README.md

@@ -1,73 +1,74 @@
----
-base_model: medicalai/ClinicalBERT
-tags:
-- generated_from_trainer
-metrics:
-- precision
-- recall
-- f1
-- accuracy
-model-index:
-- name: ClinicalBERT-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. -->
-
-# ClinicalBERT-full-finetuned-ner-pablo
-
-This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1310
-- Precision: 0.8133
-- Recall: 0.8062
-- F1: 0.8097
-- Accuracy: 0.9711
-
-## 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 |
-|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.2458        | 0.9996 | 652  | 0.1086          | 0.7977    | 0.7616 | 0.7792 | 0.9688   |
-| 0.0928        | 1.9992 | 1304 | 0.1023          | 0.8005    | 0.7676 | 0.7837 | 0.9704   |
-| 0.0706        | 2.9989 | 1956 | 0.1071          | 0.8143    | 0.7892 | 0.8015 | 0.9710   |
-| 0.04          | 4.0    | 2609 | 0.1132          | 0.8165    | 0.8046 | 0.8105 | 0.9708   |
-| 0.0313        | 4.9981 | 3260 | 0.1310          | 0.8133    | 0.8062 | 0.8097 | 0.9711   |
-
-
-### Framework versions
-
-- Transformers 4.44.0
-- Pytorch 2.4.0+cu124
-- Datasets 2.21.0
-- Tokenizers 0.19.1
+---
+library_name: transformers
+base_model: medicalai/ClinicalBERT
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: ClinicalBERT-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. -->
+
+# ClinicalBERT-full-finetuned-ner-pablo
+
+This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1104
+- Precision: 0.8025
+- Recall: 0.8001
+- F1: 0.8013
+- Accuracy: 0.9745
+
+## 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  | 0.0878          | 0.7903    | 0.7758 | 0.7830 | 0.9748   |
+| 0.1581        | 1.9980 | 505  | 0.0857          | 0.8096    | 0.7836 | 0.7964 | 0.9755   |
+| 0.1581        | 2.9990 | 758  | 0.0872          | 0.8010    | 0.7867 | 0.7938 | 0.9740   |
+| 0.0417        | 4.0    | 1011 | 0.0976          | 0.7950    | 0.8012 | 0.7981 | 0.9736   |
+| 0.0417        | 4.9852 | 1260 | 0.1104          | 0.8025    | 0.8001 | 0.8013 | 0.9745   |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

runs/Aug23_14-12-11_ee1898c059d7/events.out.tfevents.1724422332.ee1898c059d7.1664.6

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