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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: 0.8365145228215768
- name: Recall
type: recall
value: 0.8912466843501327
- name: F1
type: f1
value: 0.863013698630137
- name: Accuracy
type: accuracy
value: 0.9635817166946407
---
<!-- 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: 0.1900
- Precision: 0.8365
- Recall: 0.8912
- F1: 0.8630
- Accuracy: 0.9636
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7817 | 0.85 | 500 | 0.2275 | 0.7073 | 0.7918 | 0.7472 | 0.9392 |
| 0.2438 | 1.7 | 1000 | 0.1940 | 0.7138 | 0.8324 | 0.7686 | 0.9493 |
| 0.1652 | 2.56 | 1500 | 0.1722 | 0.7951 | 0.8678 | 0.8298 | 0.9577 |
| 0.1346 | 3.41 | 2000 | 0.1706 | 0.8049 | 0.8811 | 0.8413 | 0.9593 |
| 0.107 | 4.26 | 2500 | 0.1750 | 0.7991 | 0.8793 | 0.8373 | 0.9611 |
| 0.0851 | 5.11 | 3000 | 0.1976 | 0.7964 | 0.8820 | 0.8370 | 0.9591 |
| 0.0711 | 5.96 | 3500 | 0.1763 | 0.8195 | 0.8793 | 0.8484 | 0.9623 |
| 0.0528 | 6.81 | 4000 | 0.1883 | 0.8341 | 0.8912 | 0.8617 | 0.9632 |
| 0.0475 | 7.67 | 4500 | 0.1900 | 0.8365 | 0.8912 | 0.8630 | 0.9636 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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