End of training

README.md

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+---
+license: mit
+base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
+tags:
+- generated_from_trainer
+model-index:
+- name: BC5CDR_PubMedBERT_NER
+ 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. -->
+
+# BC5CDR_PubMedBERT_NER
+
+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: 0.0783
+- Seqeval classification report: precision recall f1-score support
+
+ Chemical 0.99 0.98 0.98 103336
+ Disease 0.76 0.86 0.81 3447
+
+ micro avg 0.98 0.98 0.98 106783
+ macro avg 0.87 0.92 0.89 106783
+weighted avg 0.98 0.98 0.98 106783
+
+
+## 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
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Seqeval classification report |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| No log | 1.0 | 143 | 0.0952 | precision recall f1-score support
+
+ Chemical 0.99 0.97 0.98 103336
+ Disease 0.68 0.88 0.76 3447
+
+ micro avg 0.97 0.97 0.97 106783
+ macro avg 0.83 0.92 0.87 106783
+weighted avg 0.98 0.97 0.97 106783
+ |
+| No log | 2.0 | 286 | 0.0804 | precision recall f1-score support
+
+ Chemical 0.99 0.98 0.98 103336
+ Disease 0.75 0.86 0.80 3447
+
+ micro avg 0.98 0.97 0.97 106783
+ macro avg 0.87 0.92 0.89 106783
+weighted avg 0.98 0.97 0.98 106783
+ |
+| No log | 3.0 | 429 | 0.0783 | precision recall f1-score support
+
+ Chemical 0.99 0.98 0.98 103336
+ Disease 0.76 0.86 0.81 3447
+
+ micro avg 0.98 0.98 0.98 106783
+ macro avg 0.87 0.92 0.89 106783
+weighted avg 0.98 0.98 0.98 106783
+ |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0