File size: 2,402 Bytes
0f966a6 |
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 |
---
language:
- mn
base_model: bayartsogt/mongolian-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-ner-demo
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. -->
# roberta-base-ner-demo
This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1227
- Precision: 0.9299
- Recall: 0.9375
- F1: 0.9337
- Accuracy: 0.9818
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1679 | 1.0 | 477 | 0.0860 | 0.8327 | 0.8878 | 0.8594 | 0.9716 |
| 0.0622 | 2.0 | 954 | 0.0739 | 0.9227 | 0.9300 | 0.9264 | 0.9807 |
| 0.029 | 3.0 | 1431 | 0.0770 | 0.9241 | 0.9347 | 0.9294 | 0.9820 |
| 0.0159 | 4.0 | 1908 | 0.0877 | 0.9327 | 0.9397 | 0.9362 | 0.9824 |
| 0.0091 | 5.0 | 2385 | 0.1074 | 0.9253 | 0.9324 | 0.9288 | 0.9805 |
| 0.0054 | 6.0 | 2862 | 0.1114 | 0.9277 | 0.9363 | 0.9320 | 0.9812 |
| 0.0042 | 7.0 | 3339 | 0.1137 | 0.9289 | 0.9359 | 0.9324 | 0.9815 |
| 0.0026 | 8.0 | 3816 | 0.1203 | 0.9301 | 0.9368 | 0.9334 | 0.9819 |
| 0.0021 | 9.0 | 4293 | 0.1222 | 0.9291 | 0.9373 | 0.9332 | 0.9818 |
| 0.0014 | 10.0 | 4770 | 0.1227 | 0.9299 | 0.9375 | 0.9337 | 0.9818 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|