End of training

README.md

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+---
+license: apache-2.0
+base_model: google-bert/bert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: BERT_BIOMAT_NER_1000
+  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. -->
+
+# BERT_BIOMAT_NER_1000
+
+This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3920
+- Precision: 0.9495
+- Recall: 0.9444
+- F1: 0.9470
+- Accuracy: 0.9380
+
+## 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: 2e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 211  | 0.2669          | 0.9407    | 0.9352 | 0.9380 | 0.9275   |
+| No log        | 2.0   | 422  | 0.2809          | 0.9457    | 0.9432 | 0.9444 | 0.9343   |
+| 0.2064        | 3.0   | 633  | 0.3114          | 0.9472    | 0.9454 | 0.9463 | 0.9353   |
+| 0.2064        | 4.0   | 844  | 0.3323          | 0.9491    | 0.9422 | 0.9456 | 0.9358   |
+| 0.0481        | 5.0   | 1055 | 0.3478          | 0.9493    | 0.9441 | 0.9467 | 0.9382   |
+| 0.0481        | 6.0   | 1266 | 0.3731          | 0.9486    | 0.9438 | 0.9462 | 0.9374   |
+| 0.0481        | 7.0   | 1477 | 0.3723          | 0.9491    | 0.9445 | 0.9468 | 0.9379   |
+| 0.0201        | 8.0   | 1688 | 0.3830          | 0.9489    | 0.9443 | 0.9466 | 0.9369   |
+| 0.0201        | 9.0   | 1899 | 0.3873          | 0.9503    | 0.9448 | 0.9475 | 0.9378   |
+| 0.0106        | 10.0  | 2110 | 0.3920          | 0.9495    | 0.9444 | 0.9470 | 0.9380   |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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