File size: 2,148 Bytes
c22ffa7 |
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 |
---
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilroberta-base-finetuned-ner-harem
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. -->
# distilroberta-base-finetuned-ner-harem
This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1882
- Precision: 0.6628
- Recall: 0.6836
- F1: 0.6730
- Accuracy: 0.9512
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 282 | 0.2799 | 0.4758 | 0.4403 | 0.4574 | 0.9202 |
| 0.3348 | 2.0 | 564 | 0.2225 | 0.5810 | 0.5940 | 0.5875 | 0.9396 |
| 0.3348 | 3.0 | 846 | 0.2105 | 0.6015 | 0.6149 | 0.6081 | 0.9389 |
| 0.1571 | 4.0 | 1128 | 0.1979 | 0.6732 | 0.6642 | 0.6687 | 0.9534 |
| 0.1571 | 5.0 | 1410 | 0.1882 | 0.6628 | 0.6836 | 0.6730 | 0.9512 |
| 0.0948 | 6.0 | 1692 | 0.2099 | 0.6196 | 0.6612 | 0.6397 | 0.9495 |
| 0.0948 | 7.0 | 1974 | 0.2251 | 0.6900 | 0.6776 | 0.6837 | 0.9540 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
|