End of training

README.md

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+---
+license: mit
+base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: MatSciBERT_BIOMAT_NER3
+  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_NER3
+
+This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3972
+- Precision: 0.5228
+- Recall: 0.7391
+- F1: 0.6124
+- Accuracy: 0.9437
+
+## 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: 16
+- eval_batch_size: 16
+- 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   | 422  | 0.2590          | 0.4873    | 0.6950 | 0.5729 | 0.9387   |
+| 0.2326        | 2.0   | 844  | 0.2598          | 0.5160    | 0.7084 | 0.5971 | 0.9428   |
+| 0.0654        | 3.0   | 1266 | 0.3152          | 0.5105    | 0.6936 | 0.5881 | 0.9430   |
+| 0.0342        | 4.0   | 1688 | 0.3075          | 0.5214    | 0.7208 | 0.6051 | 0.9432   |
+| 0.0208        | 5.0   | 2110 | 0.3623          | 0.5109    | 0.7370 | 0.6034 | 0.9421   |
+| 0.0126        | 6.0   | 2532 | 0.3504          | 0.5167    | 0.7139 | 0.5995 | 0.9428   |
+| 0.0126        | 7.0   | 2954 | 0.3708          | 0.5260    | 0.7453 | 0.6167 | 0.9445   |
+| 0.0073        | 8.0   | 3376 | 0.3898          | 0.5175    | 0.7294 | 0.6054 | 0.9432   |
+| 0.0058        | 9.0   | 3798 | 0.3917          | 0.5185    | 0.7391 | 0.6094 | 0.9432   |
+| 0.0039        | 10.0  | 4220 | 0.3972          | 0.5228    | 0.7391 | 0.6124 | 0.9437   |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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