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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: validation
args: default
metrics:
- name: Precision
type: precision
value: 0.8214447978191731
- name: Recall
type: recall
value: 0.8725868725868726
- name: F1
type: f1
value: 0.8462438567750995
- name: Accuracy
type: accuracy
value: 0.9689700130378096
---
<!-- 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: 0.1759
- Precision: 0.8214
- Recall: 0.8726
- F1: 0.8462
- Accuracy: 0.9690
## 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: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.9224 | 0.56 | 500 | 0.2309 | 0.5594 | 0.6863 | 0.6164 | 0.9358 |
| 0.2449 | 1.11 | 1000 | 0.1960 | 0.6745 | 0.8142 | 0.7378 | 0.9525 |
| 0.204 | 1.67 | 1500 | 0.1701 | 0.7256 | 0.8079 | 0.7646 | 0.9571 |
| 0.1694 | 2.22 | 2000 | 0.1526 | 0.7605 | 0.8567 | 0.8057 | 0.9640 |
| 0.1392 | 2.78 | 2500 | 0.1607 | 0.7697 | 0.8485 | 0.8072 | 0.9620 |
| 0.1191 | 3.33 | 3000 | 0.1528 | 0.7969 | 0.8596 | 0.8270 | 0.9646 |
| 0.1128 | 3.89 | 3500 | 0.1552 | 0.7668 | 0.8711 | 0.8156 | 0.9610 |
| 0.095 | 4.44 | 4000 | 0.1678 | 0.7658 | 0.8615 | 0.8108 | 0.9632 |
| 0.0979 | 5.0 | 4500 | 0.1432 | 0.8079 | 0.8625 | 0.8343 | 0.9672 |
| 0.0764 | 5.56 | 5000 | 0.1548 | 0.8098 | 0.8528 | 0.8307 | 0.9671 |
| 0.0829 | 6.11 | 5500 | 0.1423 | 0.8128 | 0.8653 | 0.8382 | 0.9672 |
| 0.0648 | 6.67 | 6000 | 0.1548 | 0.8038 | 0.8760 | 0.8383 | 0.9673 |
| 0.0529 | 7.22 | 6500 | 0.1653 | 0.8139 | 0.8716 | 0.8418 | 0.9675 |
| 0.0483 | 7.78 | 7000 | 0.1630 | 0.8186 | 0.8649 | 0.8411 | 0.9680 |
| 0.0494 | 8.33 | 7500 | 0.1709 | 0.8233 | 0.8682 | 0.8452 | 0.9686 |
| 0.0389 | 8.89 | 8000 | 0.1757 | 0.8211 | 0.8726 | 0.8460 | 0.9687 |
| 0.0356 | 9.44 | 8500 | 0.1740 | 0.8242 | 0.8736 | 0.8482 | 0.9692 |
| 0.0337 | 10.0 | 9000 | 0.1759 | 0.8214 | 0.8726 | 0.8462 | 0.9690 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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