End of training

README.md

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+---
+license: mit
+base_model: m3rg-iitd/matscibert
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: MatSciBERT_BIOMAT_NER3600
+  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. -->
+
+# MatSciBERT_BIOMAT_NER3600
+
+This model is a fine-tuned version of [m3rg-iitd/matscibert](https://huggingface.co/m3rg-iitd/matscibert) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4022
+- Precision: 0.9708
+- Recall: 0.9629
+- F1: 0.9669
+- Accuracy: 0.9638
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1486        | 1.0   | 601  | 0.2452          | 0.9584    | 0.9499 | 0.9541 | 0.9494   |
+| 0.0464        | 2.0   | 1202 | 0.2348          | 0.9658    | 0.9590 | 0.9624 | 0.9589   |
+| 0.0265        | 3.0   | 1803 | 0.2845          | 0.9659    | 0.9599 | 0.9629 | 0.9592   |
+| 0.0164        | 4.0   | 2404 | 0.3016          | 0.9689    | 0.9613 | 0.9650 | 0.9619   |
+| 0.0063        | 5.0   | 3005 | 0.3531          | 0.9699    | 0.9623 | 0.9661 | 0.9631   |
+| 0.0043        | 6.0   | 3606 | 0.3540          | 0.9701    | 0.9620 | 0.9660 | 0.9628   |
+| 0.0033        | 7.0   | 4207 | 0.3730          | 0.9708    | 0.9630 | 0.9669 | 0.9638   |
+| 0.0023        | 8.0   | 4808 | 0.3796          | 0.9710    | 0.9631 | 0.9670 | 0.9640   |
+| 0.0019        | 9.0   | 5409 | 0.3892          | 0.9712    | 0.9634 | 0.9673 | 0.9642   |
+| 0.0011        | 10.0  | 6010 | 0.4022          | 0.9708    | 0.9629 | 0.9669 | 0.9638   |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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