File size: 1,917 Bytes
988a211 fb6c2de 07428ba 988a211 07428ba 988a211 6cb47c7 988a211 6cb47c7 988a211 6cb47c7 988a211 6cb47c7 988a211 6cb47c7 |
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 |
---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: uner-muril-ner
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. -->
# uner-muril-ner
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1356
- Precision: 0.8055
- Recall: 0.8642
- F1: 0.8338
- Accuracy: 0.9637
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 144 | 0.1590 | 0.7660 | 0.7930 | 0.7793 | 0.9516 |
| No log | 2.0 | 288 | 0.1321 | 0.7907 | 0.8625 | 0.8250 | 0.9593 |
| No log | 3.0 | 432 | 0.1258 | 0.8002 | 0.8584 | 0.8283 | 0.9618 |
| 0.1493 | 4.0 | 576 | 0.1346 | 0.8009 | 0.8658 | 0.8321 | 0.9616 |
| 0.1493 | 5.0 | 720 | 0.1356 | 0.8055 | 0.8642 | 0.8338 | 0.9637 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|