File size: 3,316 Bytes
3d5dca7 |
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 81 82 83 84 85 86 |
---
license: apache-2.0
base_model: dslim/distilbert-NER
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-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-finetuned-ner
This model is a fine-tuned version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4109
- Precision: 0.6952
- Recall: 0.7549
- F1: 0.7238
- Accuracy: 0.8724
## 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: 1e-06
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.647 | 1.0 | 977 | 0.8265 | 0.4918 | 0.5741 | 0.5298 | 0.7793 |
| 0.7697 | 2.0 | 1954 | 0.6350 | 0.5801 | 0.6567 | 0.6160 | 0.8194 |
| 0.6089 | 3.0 | 2931 | 0.5591 | 0.6138 | 0.6857 | 0.6478 | 0.8352 |
| 0.534 | 4.0 | 3908 | 0.5163 | 0.6296 | 0.6955 | 0.6609 | 0.8439 |
| 0.4911 | 5.0 | 4885 | 0.4885 | 0.6436 | 0.7075 | 0.6740 | 0.8498 |
| 0.4545 | 6.0 | 5862 | 0.4683 | 0.6526 | 0.7165 | 0.6830 | 0.8557 |
| 0.4379 | 7.0 | 6839 | 0.4534 | 0.6600 | 0.7231 | 0.6901 | 0.8592 |
| 0.4124 | 8.0 | 7816 | 0.4441 | 0.6713 | 0.7274 | 0.6982 | 0.8625 |
| 0.403 | 9.0 | 8793 | 0.4345 | 0.6746 | 0.7359 | 0.7039 | 0.8658 |
| 0.394 | 10.0 | 9770 | 0.4324 | 0.6835 | 0.7445 | 0.7127 | 0.8667 |
| 0.3782 | 11.0 | 10747 | 0.4256 | 0.6820 | 0.7465 | 0.7128 | 0.8678 |
| 0.3706 | 12.0 | 11724 | 0.4213 | 0.6873 | 0.7460 | 0.7155 | 0.8691 |
| 0.3712 | 13.0 | 12701 | 0.4197 | 0.6873 | 0.7518 | 0.7181 | 0.8703 |
| 0.3626 | 14.0 | 13678 | 0.4163 | 0.6882 | 0.7523 | 0.7188 | 0.8713 |
| 0.351 | 15.0 | 14655 | 0.4142 | 0.6905 | 0.7528 | 0.7203 | 0.8717 |
| 0.3528 | 16.0 | 15632 | 0.4142 | 0.6932 | 0.7538 | 0.7222 | 0.8718 |
| 0.3523 | 17.0 | 16609 | 0.4123 | 0.6949 | 0.7533 | 0.7229 | 0.8722 |
| 0.3464 | 18.0 | 17586 | 0.4107 | 0.6936 | 0.7538 | 0.7224 | 0.8727 |
| 0.342 | 19.0 | 18563 | 0.4115 | 0.6954 | 0.7560 | 0.7244 | 0.8726 |
| 0.3496 | 20.0 | 19540 | 0.4109 | 0.6952 | 0.7549 | 0.7238 | 0.8724 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|