File size: 3,475 Bytes
ac36a09 059d85e ac36a09 059d85e ac36a09 059d85e ac36a09 059d85e ac36a09 059d85e ac36a09 |
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 |
---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: hmBERT-CoNLL-cp1
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.8690143162744776
- name: Recall
type: recall
value: 0.8887579939414338
- name: F1
type: f1
value: 0.8787752724852317
- name: Accuracy
type: accuracy
value: 0.9810170943499085
---
<!-- 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. -->
# hmBERT-CoNLL-cp1
This model is a fine-tuned version of [dbmdz/bert-base-historic-multilingual-cased](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0710
- Precision: 0.8690
- Recall: 0.8888
- F1: 0.8788
- Accuracy: 0.9810
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.06 | 25 | 0.4115 | 0.3593 | 0.3708 | 0.3649 | 0.9002 |
| No log | 0.11 | 50 | 0.2263 | 0.6360 | 0.6898 | 0.6618 | 0.9456 |
| No log | 0.17 | 75 | 0.1660 | 0.7250 | 0.7582 | 0.7412 | 0.9564 |
| No log | 0.23 | 100 | 0.1520 | 0.7432 | 0.7775 | 0.7600 | 0.9597 |
| No log | 0.28 | 125 | 0.1343 | 0.7683 | 0.8103 | 0.7888 | 0.9645 |
| No log | 0.34 | 150 | 0.1252 | 0.7973 | 0.8230 | 0.8099 | 0.9691 |
| No log | 0.4 | 175 | 0.1021 | 0.8118 | 0.8398 | 0.8255 | 0.9724 |
| No log | 0.46 | 200 | 0.1056 | 0.8153 | 0.8411 | 0.8280 | 0.9727 |
| No log | 0.51 | 225 | 0.0872 | 0.8331 | 0.8612 | 0.8469 | 0.9755 |
| No log | 0.57 | 250 | 0.1055 | 0.8226 | 0.8418 | 0.8321 | 0.9725 |
| No log | 0.63 | 275 | 0.0921 | 0.8605 | 0.8640 | 0.8623 | 0.9767 |
| No log | 0.68 | 300 | 0.0824 | 0.8600 | 0.8787 | 0.8692 | 0.9788 |
| No log | 0.74 | 325 | 0.0834 | 0.8530 | 0.8771 | 0.8649 | 0.9787 |
| No log | 0.8 | 350 | 0.0758 | 0.8646 | 0.8876 | 0.8759 | 0.9800 |
| No log | 0.85 | 375 | 0.0727 | 0.8705 | 0.8866 | 0.8784 | 0.9810 |
| No log | 0.91 | 400 | 0.0734 | 0.8717 | 0.8899 | 0.8807 | 0.9811 |
| No log | 0.97 | 425 | 0.0713 | 0.8683 | 0.8889 | 0.8785 | 0.9810 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|