File size: 2,126 Bytes
890b93e |
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 87 88 89 |
---
tags:
- generated_from_trainer
datasets:
- skript
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: skript
type: skript
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.058091286307053944
- name: Recall
type: recall
value: 0.04498714652956298
- name: F1
type: f1
value: 0.05070626584570808
- name: Accuracy
type: accuracy
value: 0.7974446689319497
---
<!-- 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-finetuned-ner
This model was trained from scratch on the skript dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6713
- Precision: 0.0581
- Recall: 0.0450
- F1: 0.0507
- Accuracy: 0.7974
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 44 | 0.8207 | 0.0 | 0.0 | 0.0 | 0.7748 |
| No log | 2.0 | 88 | 0.7113 | 0.0405 | 0.0231 | 0.0294 | 0.7889 |
| No log | 3.0 | 132 | 0.6713 | 0.0581 | 0.0450 | 0.0507 | 0.7974 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|