---
library_name: transformers
license: apache-2.0
base_model: alvaroalon2/biobert_chemical_ner
tags:
- generated_from_trainer
model-index:
- name: murat_chem_model_extra_data
  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. -->

# murat_chem_model_extra_data

This model is a fine-tuned version of [alvaroalon2/biobert_chemical_ner](https://huggingface.co/alvaroalon2/biobert_chemical_ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0089
- Chemical: {'precision': 0.9656699889258029, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539656, 'support': 900}
- Micro avg: {'precision': 0.9656699889258029, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539656, 'support': 900}
- Macro avg: {'precision': 0.9656699889258029, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539656, 'support': 900}
- Weighted avg: {'precision': 0.9656699889258028, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539658, 'support': 900}

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Chemical                                                                                                        | Micro avg                                                                                                       | Macro avg                                                                                                       | Weighted avg                                                                                                    |
|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|
| 0.0266        | 1.0   | 16198 | 0.0113          | {'precision': 0.9423503325942351, 'recall': 0.9444444444444444, 'f1-score': 0.9433962264150944, 'support': 900} | {'precision': 0.9423503325942351, 'recall': 0.9444444444444444, 'f1-score': 0.9433962264150944, 'support': 900} | {'precision': 0.9423503325942351, 'recall': 0.9444444444444444, 'f1-score': 0.9433962264150944, 'support': 900} | {'precision': 0.9423503325942351, 'recall': 0.9444444444444444, 'f1-score': 0.9433962264150944, 'support': 900} |
| 0.0092        | 2.0   | 32396 | 0.0077          | {'precision': 0.9679203539823009, 'recall': 0.9722222222222222, 'f1-score': 0.9700665188470067, 'support': 900} | {'precision': 0.9679203539823009, 'recall': 0.9722222222222222, 'f1-score': 0.9700665188470067, 'support': 900} | {'precision': 0.9679203539823009, 'recall': 0.9722222222222222, 'f1-score': 0.9700665188470067, 'support': 900} | {'precision': 0.9679203539823009, 'recall': 0.9722222222222222, 'f1-score': 0.9700665188470067, 'support': 900} |
| 0.0051        | 3.0   | 48594 | 0.0089          | {'precision': 0.9656699889258029, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539656, 'support': 900} | {'precision': 0.9656699889258029, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539656, 'support': 900} | {'precision': 0.9656699889258029, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539656, 'support': 900} | {'precision': 0.9656699889258028, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539658, 'support': 900} |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.3.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1