File size: 2,725 Bytes
d943ef8 be07b39 d943ef8 be07b39 d943ef8 be07b39 d943ef8 be07b39 d943ef8 be07b39 d943ef8 be07b39 d943ef8 be07b39 d943ef8 be07b39 d943ef8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC1_1_extended_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: 0.8551829268292683
- name: Recall
type: recall
value: 0.8995189738107964
- name: F1
type: f1
value: 0.8767908309455589
- name: Accuracy
type: accuracy
value: 0.9694414756758897
---
<!-- 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_extended_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: 0.2115
- Precision: 0.8552
- Recall: 0.8995
- F1: 0.8768
- Accuracy: 0.9694
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2948 | 1.72 | 500 | 0.1385 | 0.7752 | 0.8589 | 0.8149 | 0.9620 |
| 0.1185 | 3.44 | 1000 | 0.1411 | 0.8063 | 0.8808 | 0.8419 | 0.9692 |
| 0.0762 | 5.15 | 1500 | 0.1485 | 0.8252 | 0.8781 | 0.8509 | 0.9690 |
| 0.054 | 6.87 | 2000 | 0.1586 | 0.8368 | 0.8878 | 0.8615 | 0.9697 |
| 0.0357 | 8.59 | 2500 | 0.1774 | 0.8364 | 0.8990 | 0.8666 | 0.9705 |
| 0.026 | 10.31 | 3000 | 0.1869 | 0.8540 | 0.8974 | 0.8752 | 0.9700 |
| 0.0189 | 12.03 | 3500 | 0.2040 | 0.8555 | 0.8958 | 0.8752 | 0.9698 |
| 0.013 | 13.75 | 4000 | 0.2115 | 0.8552 | 0.8995 | 0.8768 | 0.9694 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|