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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: 0.8374155405405406
- name: Recall
type: recall
value: 0.8896366083445492
- name: F1
type: f1
value: 0.8627365673265174
- name: Accuracy
type: accuracy
value: 0.9609274366680979
---
<!-- 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: 0.2870
- Precision: 0.8374
- Recall: 0.8896
- F1: 0.8627
- Accuracy: 0.9609
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4362 | 1.7 | 500 | 0.1915 | 0.7142 | 0.8407 | 0.7723 | 0.9498 |
| 0.1873 | 3.4 | 1000 | 0.1735 | 0.7945 | 0.8793 | 0.8348 | 0.9584 |
| 0.1395 | 5.1 | 1500 | 0.1774 | 0.7771 | 0.8681 | 0.8201 | 0.9582 |
| 0.1031 | 6.8 | 2000 | 0.1837 | 0.8025 | 0.8748 | 0.8371 | 0.9582 |
| 0.0825 | 8.5 | 2500 | 0.1937 | 0.8106 | 0.8852 | 0.8462 | 0.9585 |
| 0.0671 | 10.2 | 3000 | 0.2007 | 0.8338 | 0.8932 | 0.8625 | 0.9609 |
| 0.0538 | 11.9 | 3500 | 0.2101 | 0.8222 | 0.8901 | 0.8548 | 0.9603 |
| 0.0419 | 13.61 | 4000 | 0.2177 | 0.8186 | 0.8905 | 0.8530 | 0.9619 |
| 0.0361 | 15.31 | 4500 | 0.2299 | 0.8316 | 0.8843 | 0.8571 | 0.9612 |
| 0.0281 | 17.01 | 5000 | 0.2474 | 0.8300 | 0.8825 | 0.8554 | 0.9610 |
| 0.0234 | 18.71 | 5500 | 0.2623 | 0.8327 | 0.8843 | 0.8577 | 0.9606 |
| 0.0194 | 20.41 | 6000 | 0.2702 | 0.8311 | 0.8829 | 0.8562 | 0.9603 |
| 0.0169 | 22.11 | 6500 | 0.2781 | 0.8358 | 0.8883 | 0.8612 | 0.9608 |
| 0.0151 | 23.81 | 7000 | 0.2870 | 0.8374 | 0.8896 | 0.8627 | 0.9609 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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