File size: 2,757 Bytes
0880b92 2967d6f 0880b92 |
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 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
args: en
metrics:
- name: Precision
type: precision
value: 0.819622641509434
- name: Recall
type: recall
value: 0.8444790046656299
- name: F1
type: f1
value: 0.8318651857525853
- name: Accuracy
type: accuracy
value: 0.9269227060339613
- task:
type: token-classification
name: Token Classification
dataset:
name: wikiann
type: wikiann
config: en
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.8492771401033908
verified: true
- name: Precision
type: precision
value: 0.857294905524994
verified: true
- name: Recall
type: recall
value: 0.865900059186607
verified: true
- name: F1
type: f1
value: 0.8615759964905745
verified: true
- name: loss
type: loss
value: 1.054654836654663
verified: true
---
<!-- 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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3217
- Precision: 0.8196
- Recall: 0.8445
- F1: 0.8319
- Accuracy: 0.9269
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2821 | 1.0 | 2500 | 0.2906 | 0.7983 | 0.8227 | 0.8103 | 0.9193 |
| 0.2087 | 2.0 | 5000 | 0.2614 | 0.8030 | 0.8379 | 0.8201 | 0.9257 |
| 0.1404 | 3.0 | 7500 | 0.3217 | 0.8196 | 0.8445 | 0.8319 | 0.9269 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|