---
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: 0.6069
- Precision: 0.7971
- Recall: 0.8633
- F1: 0.8289
- Accuracy: 0.7973

## 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: 4
- eval_batch_size: 4
- 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        | 0.37  | 100  | 0.7456          | 0.7807    | 0.8460 | 0.8120 | 0.7807   |
| No log        | 0.75  | 200  | 0.7377          | 0.7807    | 0.8460 | 0.8120 | 0.7807   |
| No log        | 1.12  | 300  | 0.7192          | 0.7807    | 0.8460 | 0.8120 | 0.7807   |
| No log        | 1.49  | 400  | 0.7027          | 0.7808    | 0.8461 | 0.8122 | 0.7808   |
| 0.8312        | 1.87  | 500  | 0.6920          | 0.7817    | 0.8471 | 0.8131 | 0.7817   |
| 0.8312        | 2.24  | 600  | 0.6785          | 0.7813    | 0.8466 | 0.8126 | 0.7813   |
| 0.8312        | 2.61  | 700  | 0.6826          | 0.7826    | 0.8480 | 0.8140 | 0.7826   |
| 0.8312        | 2.99  | 800  | 0.6590          | 0.7855    | 0.8511 | 0.8170 | 0.7855   |
| 0.8312        | 3.36  | 900  | 0.6499          | 0.7904    | 0.8564 | 0.8221 | 0.7904   |
| 0.755         | 3.73  | 1000 | 0.6440          | 0.7933    | 0.8596 | 0.8251 | 0.7933   |
| 0.755         | 4.1   | 1100 | 0.6378          | 0.7941    | 0.8605 | 0.8260 | 0.7943   |
| 0.755         | 4.48  | 1200 | 0.6291          | 0.7950    | 0.8615 | 0.8269 | 0.7951   |
| 0.755         | 4.85  | 1300 | 0.6269          | 0.7927    | 0.8588 | 0.8244 | 0.7927   |
| 0.755         | 5.22  | 1400 | 0.6202          | 0.7949    | 0.8613 | 0.8267 | 0.7950   |
| 0.7217        | 5.6   | 1500 | 0.6154          | 0.7962    | 0.8627 | 0.8281 | 0.7963   |
| 0.7217        | 5.97  | 1600 | 0.6126          | 0.7964    | 0.8627 | 0.8282 | 0.7966   |
| 0.7217        | 6.34  | 1700 | 0.6099          | 0.7960    | 0.8624 | 0.8279 | 0.7960   |
| 0.7217        | 6.72  | 1800 | 0.6094          | 0.7926    | 0.8580 | 0.8240 | 0.7933   |
| 0.7217        | 7.09  | 1900 | 0.6087          | 0.7964    | 0.8629 | 0.8283 | 0.7966   |
| 0.6873        | 7.46  | 2000 | 0.6069          | 0.7971    | 0.8633 | 0.8289 | 0.7973   |
| 0.6873        | 7.84  | 2100 | 0.6048          | 0.7968    | 0.8629 | 0.8285 | 0.7970   |
| 0.6873        | 8.21  | 2200 | 0.6017          | 0.7974    | 0.8630 | 0.8289 | 0.7973   |
| 0.6873        | 8.58  | 2300 | 0.6025          | 0.7941    | 0.8591 | 0.8253 | 0.7951   |
| 0.6873        | 8.96  | 2400 | 0.5981          | 0.7964    | 0.8616 | 0.8277 | 0.7964   |
| 0.6731        | 9.33  | 2500 | 0.5993          | 0.7968    | 0.8622 | 0.8282 | 0.7969   |


### Framework versions

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