File size: 3,277 Bytes
d943ef8 af45739 d943ef8 be07b39 d943ef8 af45739 d943ef8 af45739 d943ef8 af45739 be07b39 af45739 d943ef8 be07b39 d943ef8 af45739 d943ef8 af45739 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 99 100 101 102 103 104 |
---
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: 0.8595505617977528
- name: Recall
type: recall
value: 0.8995189738107964
- name: F1
type: f1
value: 0.8790806999216505
- name: Accuracy
type: accuracy
value: 0.9695206428373511
---
<!-- 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: 0.2397
- Precision: 0.8596
- Recall: 0.8995
- F1: 0.8791
- Accuracy: 0.9695
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3533 | 1.72 | 500 | 0.1415 | 0.7483 | 0.8439 | 0.7933 | 0.9609 |
| 0.1509 | 3.44 | 1000 | 0.1352 | 0.8073 | 0.8685 | 0.8368 | 0.9664 |
| 0.1072 | 5.15 | 1500 | 0.1533 | 0.8151 | 0.8739 | 0.8434 | 0.9674 |
| 0.0778 | 6.87 | 2000 | 0.1740 | 0.8400 | 0.8781 | 0.8586 | 0.9668 |
| 0.059 | 8.59 | 2500 | 0.1676 | 0.8365 | 0.8942 | 0.8644 | 0.9699 |
| 0.0475 | 10.31 | 3000 | 0.1699 | 0.8295 | 0.8813 | 0.8546 | 0.9678 |
| 0.0381 | 12.03 | 3500 | 0.1876 | 0.8418 | 0.8985 | 0.8692 | 0.9686 |
| 0.0287 | 13.75 | 4000 | 0.2100 | 0.8446 | 0.8979 | 0.8705 | 0.9681 |
| 0.0238 | 15.46 | 4500 | 0.2007 | 0.8466 | 0.8995 | 0.8722 | 0.9702 |
| 0.0186 | 17.18 | 5000 | 0.2201 | 0.8568 | 0.8926 | 0.8743 | 0.9689 |
| 0.0161 | 18.9 | 5500 | 0.2200 | 0.8573 | 0.8990 | 0.8776 | 0.9700 |
| 0.014 | 20.62 | 6000 | 0.2326 | 0.8601 | 0.8974 | 0.8784 | 0.9697 |
| 0.0104 | 22.34 | 6500 | 0.2370 | 0.8639 | 0.8990 | 0.8811 | 0.9696 |
| 0.0099 | 24.05 | 7000 | 0.2397 | 0.8596 | 0.8995 | 0.8791 | 0.9695 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|