---
license: mit
base_model: surrey-nlp/roberta-base-finetuned-abbr
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-finetuned-abbr-finetuned-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. -->

# roberta-base-finetuned-abbr-finetuned-ner

This model is a fine-tuned version of [surrey-nlp/roberta-base-finetuned-abbr](https://huggingface.co/surrey-nlp/roberta-base-finetuned-abbr) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1753
- Precision: 0.9674
- Recall: 0.9681
- F1: 0.9678
- Accuracy: 0.9618

## 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-06
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.6   | 10   | 0.9865          | 0.7657    | 0.7982 | 0.7816 | 0.7573   |
| No log        | 1.19  | 20   | 0.7172          | 0.8161    | 0.8566 | 0.8359 | 0.8204   |
| No log        | 1.79  | 30   | 0.5382          | 0.8437    | 0.8759 | 0.8595 | 0.8478   |
| No log        | 2.39  | 40   | 0.4196          | 0.8713    | 0.8938 | 0.8824 | 0.8733   |
| No log        | 2.99  | 50   | 0.3485          | 0.8965    | 0.9112 | 0.9038 | 0.8979   |
| No log        | 3.58  | 60   | 0.3031          | 0.9241    | 0.9325 | 0.9283 | 0.9218   |
| No log        | 4.18  | 70   | 0.2688          | 0.9459    | 0.9496 | 0.9477 | 0.9411   |
| No log        | 4.78  | 80   | 0.2434          | 0.9531    | 0.9559 | 0.9545 | 0.9481   |
| No log        | 5.37  | 90   | 0.2235          | 0.9605    | 0.9623 | 0.9614 | 0.9555   |
| No log        | 5.97  | 100  | 0.2078          | 0.9612    | 0.9623 | 0.9618 | 0.9559   |
| No log        | 6.57  | 110  | 0.1966          | 0.9637    | 0.9647 | 0.9642 | 0.9580   |
| No log        | 7.16  | 120  | 0.1879          | 0.9646    | 0.9655 | 0.9651 | 0.9591   |
| No log        | 7.76  | 130  | 0.1821          | 0.9664    | 0.9671 | 0.9667 | 0.9608   |
| No log        | 8.36  | 140  | 0.1782          | 0.9669    | 0.9676 | 0.9673 | 0.9613   |
| No log        | 8.96  | 150  | 0.1760          | 0.9674    | 0.9683 | 0.9679 | 0.9618   |
| No log        | 9.55  | 160  | 0.1753          | 0.9674    | 0.9681 | 0.9678 | 0.9618   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2