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---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scenario-TCR-NER_data-univner_half
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. -->
# scenario-TCR-NER_data-univner_half
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1160
- Precision: 0.8555
- Recall: 0.8189
- F1: 0.8368
- Accuracy: 0.9828
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1189 | 0.58 | 500 | 0.0623 | 0.8010 | 0.8531 | 0.8262 | 0.9822 |
| 0.0469 | 1.17 | 1000 | 0.0640 | 0.8246 | 0.8567 | 0.8404 | 0.9833 |
| 0.0348 | 1.75 | 1500 | 0.0668 | 0.8335 | 0.8550 | 0.8441 | 0.9834 |
| 0.0242 | 2.33 | 2000 | 0.0734 | 0.8202 | 0.8538 | 0.8367 | 0.9826 |
| 0.0215 | 2.91 | 2500 | 0.0717 | 0.8455 | 0.8598 | 0.8526 | 0.9843 |
| 0.0142 | 3.5 | 3000 | 0.0802 | 0.8383 | 0.8424 | 0.8404 | 0.9836 |
| 0.0144 | 4.08 | 3500 | 0.0836 | 0.8443 | 0.8554 | 0.8499 | 0.9843 |
| 0.0103 | 4.66 | 4000 | 0.0811 | 0.8479 | 0.8590 | 0.8534 | 0.9844 |
| 0.0087 | 5.24 | 4500 | 0.0887 | 0.8364 | 0.8628 | 0.8494 | 0.9840 |
| 0.0092 | 5.83 | 5000 | 0.0876 | 0.8367 | 0.8430 | 0.8399 | 0.9833 |
| 0.0076 | 6.41 | 5500 | 0.1004 | 0.8440 | 0.8495 | 0.8468 | 0.9841 |
| 0.007 | 6.99 | 6000 | 0.1080 | 0.8215 | 0.8518 | 0.8364 | 0.9830 |
| 0.0055 | 7.58 | 6500 | 0.0988 | 0.8454 | 0.8358 | 0.8406 | 0.9831 |
| 0.0055 | 8.16 | 7000 | 0.0950 | 0.8485 | 0.8461 | 0.8473 | 0.9839 |
| 0.0044 | 8.74 | 7500 | 0.1001 | 0.8456 | 0.8414 | 0.8435 | 0.9836 |
| 0.004 | 9.32 | 8000 | 0.1084 | 0.8340 | 0.8495 | 0.8417 | 0.9834 |
| 0.004 | 9.91 | 8500 | 0.1175 | 0.8351 | 0.8505 | 0.8427 | 0.9829 |
| 0.0033 | 10.49 | 9000 | 0.1160 | 0.8555 | 0.8189 | 0.8368 | 0.9828 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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