metadata
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: indic-transformers-te-distilbert
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
args: te
metrics:
- name: Precision
type: precision
value: 0.5657225853304285
- name: Recall
type: recall
value: 0.6486261448792673
- name: F1
type: f1
value: 0.604344453064391
- name: Accuracy
type: accuracy
value: 0.9049186160277506
indic-transformers-te-distilbert
This model was trained from scratch on the wikiann dataset. It achieves the following results on the evaluation set:
- Loss: 0.2940
- Precision: 0.5657
- Recall: 0.6486
- F1: 0.6043
- Accuracy: 0.9049
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 125 | 0.3629 | 0.4855 | 0.5287 | 0.5062 | 0.8826 |
| No log | 2.0 | 250 | 0.3032 | 0.5446 | 0.6303 | 0.5843 | 0.9002 |
| No log | 3.0 | 375 | 0.2940 | 0.5657 | 0.6486 | 0.6043 | 0.9049 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3