---
base_model: FacebookAI/xlm-roberta-base
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-kd-pre-ner-full-xlmr_data-univner_full66
  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-kd-pre-ner-full-xlmr_data-univner_full66

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 48.2869
- Precision: 0.8199
- Recall: 0.8357
- F1: 0.8277
- Accuracy: 0.9820

## 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: 8
- eval_batch_size: 32
- seed: 66
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 125.1691      | 0.2911 | 500   | 87.0081         | 0.7214    | 0.7142 | 0.7178 | 0.9733   |
| 79.398        | 0.5822 | 1000  | 74.2576         | 0.7483    | 0.7873 | 0.7673 | 0.9766   |
| 71.1356       | 0.8732 | 1500  | 69.3294         | 0.7983    | 0.7680 | 0.7829 | 0.9781   |
| 66.3695       | 1.1643 | 2000  | 65.7687         | 0.7888    | 0.8071 | 0.7978 | 0.9797   |
| 63.0397       | 1.4554 | 2500  | 63.3432         | 0.7887    | 0.8071 | 0.7978 | 0.9795   |
| 60.9741       | 1.7465 | 3000  | 61.3539         | 0.8072    | 0.8042 | 0.8057 | 0.9801   |
| 59.1711       | 2.0375 | 3500  | 59.9207         | 0.7996    | 0.8064 | 0.8030 | 0.9800   |
| 56.9342       | 2.3286 | 4000  | 58.2970         | 0.7933    | 0.8205 | 0.8067 | 0.9805   |
| 55.7631       | 2.6197 | 4500  | 57.1782         | 0.8095    | 0.8228 | 0.8161 | 0.9811   |
| 54.9048       | 2.9108 | 5000  | 56.2284         | 0.8047    | 0.8214 | 0.8129 | 0.9809   |
| 53.4488       | 3.2019 | 5500  | 55.2710         | 0.8025    | 0.8282 | 0.8151 | 0.9811   |
| 52.5197       | 3.4929 | 6000  | 54.6004         | 0.8184    | 0.8101 | 0.8142 | 0.9809   |
| 51.7352       | 3.7840 | 6500  | 53.8065         | 0.8014    | 0.8316 | 0.8163 | 0.9807   |
| 51.0298       | 4.0751 | 7000  | 53.1329         | 0.8129    | 0.8261 | 0.8195 | 0.9812   |
| 50.2097       | 4.3662 | 7500  | 52.4983         | 0.8155    | 0.8272 | 0.8213 | 0.9815   |
| 49.7445       | 4.6573 | 8000  | 52.1048         | 0.8134    | 0.8225 | 0.8179 | 0.9814   |
| 49.2896       | 4.9483 | 8500  | 51.5581         | 0.8169    | 0.8335 | 0.8251 | 0.9818   |
| 48.7639       | 5.2394 | 9000  | 51.2521         | 0.8145    | 0.8282 | 0.8213 | 0.9817   |
| 48.2298       | 5.5305 | 9500  | 50.7723         | 0.8134    | 0.8295 | 0.8213 | 0.9816   |
| 47.942        | 5.8216 | 10000 | 50.5179         | 0.8131    | 0.8323 | 0.8226 | 0.9817   |
| 47.4768       | 6.1126 | 10500 | 50.1601         | 0.8179    | 0.8322 | 0.8250 | 0.9818   |
| 47.176        | 6.4037 | 11000 | 49.9205         | 0.8154    | 0.8335 | 0.8243 | 0.9819   |
| 46.9979       | 6.6948 | 11500 | 49.6460         | 0.8201    | 0.8321 | 0.8260 | 0.9819   |
| 46.7367       | 6.9859 | 12000 | 49.4427         | 0.8145    | 0.8310 | 0.8227 | 0.9815   |
| 46.3668       | 7.2770 | 12500 | 49.3164         | 0.8139    | 0.8287 | 0.8213 | 0.9815   |
| 46.1975       | 7.5680 | 13000 | 49.0327         | 0.8137    | 0.8336 | 0.8235 | 0.9817   |
| 46.1108       | 7.8591 | 13500 | 48.9452         | 0.8249    | 0.8298 | 0.8273 | 0.9819   |
| 45.8657       | 8.1502 | 14000 | 48.7530         | 0.8234    | 0.8352 | 0.8293 | 0.9823   |
| 45.6956       | 8.4413 | 14500 | 48.5710         | 0.8210    | 0.8387 | 0.8298 | 0.9823   |
| 45.6259       | 8.7324 | 15000 | 48.5836         | 0.8200    | 0.8325 | 0.8262 | 0.9818   |
| 45.5612       | 9.0234 | 15500 | 48.4547         | 0.8214    | 0.8341 | 0.8277 | 0.9822   |
| 45.4012       | 9.3145 | 16000 | 48.3506         | 0.8224    | 0.8373 | 0.8298 | 0.9821   |
| 45.3943       | 9.6056 | 16500 | 48.3722         | 0.8207    | 0.8325 | 0.8265 | 0.9820   |
| 45.3452       | 9.8967 | 17000 | 48.2869         | 0.8199    | 0.8357 | 0.8277 | 0.9820   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1