File size: 2,440 Bytes
e0a1b9e 60a9f43 e0a1b9e 60a9f43 e0a1b9e 60a9f43 e0a1b9e 60a9f43 e0a1b9e |
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 |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2228
- Precision: 0.8030
- Recall: 0.8093
- F1: 0.8061
- Accuracy: 0.9545
## 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: 0.0005
- 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
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3032 | 1.0 | 878 | 0.3241 | 0.6979 | 0.5912 | 0.6401 | 0.9168 |
| 0.2666 | 2.0 | 1756 | 0.2822 | 0.6475 | 0.6577 | 0.6525 | 0.9221 |
| 0.2025 | 3.0 | 2634 | 0.2402 | 0.7021 | 0.7273 | 0.7144 | 0.9369 |
| 0.1421 | 4.0 | 3512 | 0.2158 | 0.7283 | 0.7331 | 0.7307 | 0.9390 |
| 0.111 | 5.0 | 4390 | 0.2189 | 0.7442 | 0.7395 | 0.7418 | 0.9417 |
| 0.0813 | 6.0 | 5268 | 0.2196 | 0.7307 | 0.7812 | 0.7551 | 0.9442 |
| 0.0538 | 7.0 | 6146 | 0.2169 | 0.7594 | 0.8049 | 0.7815 | 0.9497 |
| 0.0389 | 8.0 | 7024 | 0.2133 | 0.7929 | 0.7991 | 0.7960 | 0.9520 |
| 0.0263 | 9.0 | 7902 | 0.2192 | 0.8002 | 0.7991 | 0.7996 | 0.9530 |
| 0.0141 | 10.0 | 8780 | 0.2224 | 0.8029 | 0.8097 | 0.8063 | 0.9546 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|