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---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC2_0_Supertypes_xlm-roberta-large
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cnec
type: cnec
config: default
split: test
args: default
metrics:
- name: Precision
type: precision
value: 0.7760029717682021
- name: Recall
type: recall
value: 0.8582580115036976
- name: F1
type: f1
value: 0.8150604760046821
- name: Accuracy
type: accuracy
value: 0.9631292359381336
---
<!-- 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. -->
# CNEC2_0_Supertypes_xlm-roberta-large
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1727
- Precision: 0.7760
- Recall: 0.8583
- F1: 0.8151
- Accuracy: 0.9631
## 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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.9465 | 0.56 | 500 | 0.2705 | 0.4955 | 0.6754 | 0.5716 | 0.9281 |
| 0.2305 | 1.11 | 1000 | 0.1836 | 0.7054 | 0.8205 | 0.7586 | 0.9539 |
| 0.179 | 1.67 | 1500 | 0.1784 | 0.7485 | 0.8180 | 0.7817 | 0.9576 |
| 0.1484 | 2.22 | 2000 | 0.1835 | 0.7571 | 0.8578 | 0.8043 | 0.9615 |
| 0.1283 | 2.78 | 2500 | 0.1792 | 0.7333 | 0.8135 | 0.7713 | 0.9596 |
| 0.1092 | 3.33 | 3000 | 0.1749 | 0.7707 | 0.8422 | 0.8049 | 0.9619 |
| 0.0963 | 3.89 | 3500 | 0.1706 | 0.7711 | 0.8537 | 0.8103 | 0.9633 |
| 0.0845 | 4.44 | 4000 | 0.1709 | 0.7811 | 0.8517 | 0.8149 | 0.9633 |
| 0.0801 | 5.0 | 4500 | 0.1727 | 0.7760 | 0.8583 | 0.8151 | 0.9631 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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