File size: 2,198 Bytes
a42c5df |
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 |
---
library_name: transformers
language:
- uz
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- risqaliyevds/uzbek_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Uzbek NER model
results: []
---
<!-- 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. -->
# Uzbek NER model
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the Uzbek Ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1421
- Precision: 0.6071
- Recall: 0.6482
- F1: 0.6270
- Accuracy: 0.9486
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1671 | 0.5758 | 150 | 0.1632 | 0.5260 | 0.6425 | 0.5785 | 0.9402 |
| 0.1453 | 1.1497 | 300 | 0.1481 | 0.5935 | 0.6191 | 0.6061 | 0.9467 |
| 0.134 | 1.7255 | 450 | 0.1449 | 0.5936 | 0.6216 | 0.6073 | 0.9480 |
| 0.1273 | 2.2994 | 600 | 0.1413 | 0.6217 | 0.6262 | 0.6239 | 0.9493 |
| 0.1258 | 2.8752 | 750 | 0.1421 | 0.6071 | 0.6482 | 0.6270 | 0.9486 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.1.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|