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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: CNEC1_1_Supertypes_xlm-roberta-large
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cnec
type: cnec
config: default
split: validation
args: default
metrics:
- name: Precision
type: precision
value: 0.8251895534962089
- name: Recall
type: recall
value: 0.8788694481830417
- name: F1
type: f1
value: 0.8511840104279819
- name: Accuracy
type: accuracy
value: 0.9608493696084937
---
<!-- 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. -->
# CNEC1_1_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: 0.2044
- Precision: 0.8252
- Recall: 0.8789
- F1: 0.8512
- Accuracy: 0.9608
## 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
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4123 | 0.85 | 500 | 0.2026 | 0.7055 | 0.8255 | 0.7608 | 0.9474 |
| 0.196 | 1.7 | 1000 | 0.1791 | 0.7699 | 0.8573 | 0.8113 | 0.9543 |
| 0.145 | 2.56 | 1500 | 0.1962 | 0.7604 | 0.8430 | 0.7996 | 0.9533 |
| 0.1184 | 3.41 | 2000 | 0.1812 | 0.7897 | 0.8708 | 0.8282 | 0.9569 |
| 0.0959 | 4.26 | 2500 | 0.1788 | 0.7989 | 0.8681 | 0.8321 | 0.9601 |
| 0.0707 | 5.11 | 3000 | 0.1868 | 0.8106 | 0.8852 | 0.8462 | 0.9616 |
| 0.0561 | 5.96 | 3500 | 0.1988 | 0.8132 | 0.8730 | 0.8421 | 0.9596 |
| 0.0404 | 6.81 | 4000 | 0.2027 | 0.8268 | 0.8847 | 0.8548 | 0.9614 |
| 0.0383 | 7.67 | 4500 | 0.2044 | 0.8252 | 0.8789 | 0.8512 | 0.9608 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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