File size: 4,041 Bytes
139d41e 0310483 139d41e 0310483 139d41e 0310483 139d41e 0310483 139d41e 0310483 139d41e ca42a6c 0310483 139d41e 0310483 139d41e 0310483 139d41e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
---
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: 0.8427382053654024
- name: Recall
type: recall
value: 0.8793436293436293
- name: F1
type: f1
value: 0.8606518658478979
- name: Accuracy
type: accuracy
value: 0.9671736925974214
---
<!-- 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: 0.2674
- Precision: 0.8427
- Recall: 0.8793
- F1: 0.8607
- Accuracy: 0.9672
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5221 | 1.11 | 500 | 0.1718 | 0.6648 | 0.8012 | 0.7266 | 0.9535 |
| 0.1777 | 2.22 | 1000 | 0.1397 | 0.7499 | 0.8393 | 0.7921 | 0.9627 |
| 0.1321 | 3.33 | 1500 | 0.1383 | 0.7760 | 0.8711 | 0.8208 | 0.9655 |
| 0.1132 | 4.44 | 2000 | 0.1456 | 0.7646 | 0.8542 | 0.8069 | 0.9636 |
| 0.1008 | 5.56 | 2500 | 0.1442 | 0.7750 | 0.8692 | 0.8194 | 0.9648 |
| 0.0782 | 6.67 | 3000 | 0.1516 | 0.8107 | 0.8663 | 0.8376 | 0.9657 |
| 0.0692 | 7.78 | 3500 | 0.1690 | 0.8023 | 0.8620 | 0.8311 | 0.9660 |
| 0.0582 | 8.89 | 4000 | 0.1591 | 0.8125 | 0.8847 | 0.8470 | 0.9672 |
| 0.0511 | 10.0 | 4500 | 0.1813 | 0.8033 | 0.8832 | 0.8414 | 0.9661 |
| 0.0432 | 11.11 | 5000 | 0.1833 | 0.8231 | 0.8822 | 0.8516 | 0.9669 |
| 0.0381 | 12.22 | 5500 | 0.2097 | 0.8062 | 0.8634 | 0.8338 | 0.9659 |
| 0.0328 | 13.33 | 6000 | 0.2043 | 0.8026 | 0.8711 | 0.8355 | 0.9661 |
| 0.0292 | 14.44 | 6500 | 0.2217 | 0.8255 | 0.8769 | 0.8505 | 0.9669 |
| 0.0247 | 15.56 | 7000 | 0.2411 | 0.8297 | 0.8745 | 0.8515 | 0.9667 |
| 0.0206 | 16.67 | 7500 | 0.2425 | 0.8255 | 0.8764 | 0.8502 | 0.9663 |
| 0.0184 | 17.78 | 8000 | 0.2405 | 0.8329 | 0.8586 | 0.8455 | 0.9668 |
| 0.0157 | 18.89 | 8500 | 0.2521 | 0.8314 | 0.8832 | 0.8565 | 0.9677 |
| 0.0134 | 20.0 | 9000 | 0.2504 | 0.8349 | 0.8764 | 0.8552 | 0.9671 |
| 0.0116 | 21.11 | 9500 | 0.2570 | 0.8344 | 0.8779 | 0.8556 | 0.9678 |
| 0.0109 | 22.22 | 10000 | 0.2570 | 0.8320 | 0.8793 | 0.8550 | 0.9677 |
| 0.0093 | 23.33 | 10500 | 0.2639 | 0.8373 | 0.8793 | 0.8578 | 0.9674 |
| 0.0086 | 24.44 | 11000 | 0.2674 | 0.8427 | 0.8793 | 0.8607 | 0.9672 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|