Training complete

README.md

@@ -1,73 +1,72 @@
----
-base_model: allenai/biomed_roberta_base
-tags:
-- generated_from_trainer
-metrics:
-- precision
-- recall
-- f1
-- accuracy
-model-index:
-- name: BioMedRoBERTa-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. -->
-
-# BioMedRoBERTa-full-finetuned-ner-pablo
-
-This model is a fine-tuned version of [allenai/biomed_roberta_base](https://huggingface.co/allenai/biomed_roberta_base) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1128
-- Precision: 0.8205
-- Recall: 0.8113
-- F1: 0.8159
-- Accuracy: 0.9729
-
-## 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 |
-|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.2807        | 0.9996 | 652  | 0.1084          | 0.8072    | 0.7593 | 0.7825 | 0.9703   |
-| 0.099         | 1.9992 | 1304 | 0.0994          | 0.7885    | 0.7819 | 0.7852 | 0.9704   |
-| 0.0781        | 2.9989 | 1956 | 0.0902          | 0.8206    | 0.7904 | 0.8052 | 0.9727   |
-| 0.049         | 4.0    | 2609 | 0.0997          | 0.8223    | 0.8101 | 0.8161 | 0.9731   |
-| 0.0386        | 4.9981 | 3260 | 0.1128          | 0.8205    | 0.8113 | 0.8159 | 0.9729   |
-
-
-### Framework versions
-
-- Transformers 4.44.0
-- Pytorch 2.4.0+cu124
-- Datasets 2.21.0
-- Tokenizers 0.19.1
+---
+library_name: transformers
+base_model: allenai/biomed_roberta_base
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: BioMedRoBERTa-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. -->
+
+# BioMedRoBERTa-full-finetuned-ner-pablo
+
+This model is a fine-tuned version of [allenai/biomed_roberta_base](https://huggingface.co/allenai/biomed_roberta_base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0957
+- Precision: 0.8174
+- Recall: 0.8204
+- F1: 0.8189
+- Accuracy: 0.9769
+
+## 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: 0.0001
+- train_batch_size: 128
+- eval_batch_size: 128
+- seed: 42
+- 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        | 1.0   | 427  | 0.0818          | 0.8084    | 0.7885 | 0.7983 | 0.9756   |
+| 0.263         | 2.0   | 854  | 0.0760          | 0.8029    | 0.8081 | 0.8055 | 0.9766   |
+| 0.0608        | 3.0   | 1281 | 0.0818          | 0.7963    | 0.8199 | 0.8079 | 0.9754   |
+| 0.046         | 4.0   | 1708 | 0.0904          | 0.8048    | 0.8232 | 0.8139 | 0.9759   |
+| 0.0327        | 5.0   | 2135 | 0.0957          | 0.8174    | 0.8204 | 0.8189 | 0.9769   |
+
+
+### Framework versions
+
+- Transformers 4.44.1
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

runs/Aug22_08-42-19_26b01926c00f/events.out.tfevents.1724316140.26b01926c00f.1526.2

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