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

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: 0.3827
- Chemical: {'precision': 0.8843176605504587, 'recall': 0.8439124487004104, 'f1': 0.863642727145457, 'number': 7310}
- Overall Precision: 0.8843
- Overall Recall: 0.8439
- Overall F1: 0.8636
- Overall Accuracy: 0.9447

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Chemical                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.6817        | 1.2048  | 100  | 0.3090          | {'precision': 0.8702002710435175, 'recall': 0.7905608755129959, 'f1': 0.828471077342126, 'number': 7310}  | 0.8702            | 0.7906         | 0.8285     | 0.9204           |
| 1.6817        | 2.4096  | 200  | 0.2900          | {'precision': 0.8720861611094718, 'recall': 0.8086183310533516, 'f1': 0.8391538898353209, 'number': 7310} | 0.8721            | 0.8086         | 0.8392     | 0.9312           |
| 1.6817        | 3.6145  | 300  | 0.3071          | {'precision': 0.8830255057167986, 'recall': 0.824076607387141, 'f1': 0.8525332578545147, 'number': 7310}  | 0.8830            | 0.8241         | 0.8525     | 0.9398           |
| 1.6817        | 4.8193  | 400  | 0.3099          | {'precision': 0.882646325363063, 'recall': 0.8231190150478797, 'f1': 0.8518439866921499, 'number': 7310}  | 0.8826            | 0.8231         | 0.8518     | 0.9411           |
| 0.1018        | 6.0241  | 500  | 0.3891          | {'precision': 0.8816456613066782, 'recall': 0.8325581395348837, 'f1': 0.8563990712727785, 'number': 7310} | 0.8816            | 0.8326         | 0.8564     | 0.9402           |
| 0.1018        | 7.2289  | 600  | 0.3672          | {'precision': 0.8851640513552068, 'recall': 0.8488372093023255, 'f1': 0.8666201117318435, 'number': 7310} | 0.8852            | 0.8488         | 0.8666     | 0.9451           |
| 0.1018        | 8.4337  | 700  | 0.3459          | {'precision': 0.8812949640287769, 'recall': 0.8378932968536251, 'f1': 0.8590462833099579, 'number': 7310} | 0.8813            | 0.8379         | 0.8590     | 0.9449           |
| 0.1018        | 9.6386  | 800  | 0.3601          | {'precision': 0.880656108597285, 'recall': 0.8519835841313269, 'f1': 0.8660826032540675, 'number': 7310}  | 0.8807            | 0.8520         | 0.8661     | 0.9462           |
| 0.1018        | 10.8434 | 900  | 0.3711          | {'precision': 0.881471972614463, 'recall': 0.8454172366621067, 'f1': 0.8630682214929124, 'number': 7310}  | 0.8815            | 0.8454         | 0.8631     | 0.9443           |
| 0.0038        | 12.0482 | 1000 | 0.3779          | {'precision': 0.8816542644533486, 'recall': 0.8428180574555404, 'f1': 0.8617988529864317, 'number': 7310} | 0.8817            | 0.8428         | 0.8618     | 0.9437           |
| 0.0038        | 13.2530 | 1100 | 0.3829          | {'precision': 0.8837275985663082, 'recall': 0.8432284541723666, 'f1': 0.863003150157508, 'number': 7310}  | 0.8837            | 0.8432         | 0.8630     | 0.9447           |
| 0.0038        | 14.4578 | 1200 | 0.3827          | {'precision': 0.8843176605504587, 'recall': 0.8439124487004104, 'f1': 0.863642727145457, 'number': 7310}  | 0.8843            | 0.8439         | 0.8636     | 0.9447           |


### Framework versions

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