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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
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