metadata
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ner
type: ner
config: indian_names
split: train
args: indian_names
metrics:
- name: Precision
type: precision
value: 0.9998842940781709
- name: Recall
type: recall
value: 0.9998380192062941
- name: F1
type: f1
value: 0.9998611561068173
- name: Accuracy
type: accuracy
value: 0.999938944347773
my_awesome_wnut_model
This model is a fine-tuned version of bert-base-multilingual-cased on the ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.0001
- Precision: 0.9999
- Recall: 0.9998
- F1: 0.9999
- Accuracy: 0.9999
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: 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0342 | 1.0 | 688 | 0.0063 | 0.9950 | 0.9917 | 0.9934 | 0.9956 |
| 0.0117 | 2.0 | 1376 | 0.0015 | 0.9979 | 0.9974 | 0.9977 | 0.9988 |
| 0.0049 | 3.0 | 2064 | 0.0006 | 0.9991 | 0.9994 | 0.9992 | 0.9995 |
| 0.0017 | 4.0 | 2752 | 0.0001 | 0.9997 | 0.9997 | 0.9997 | 0.9999 |
| 0.001 | 5.0 | 3440 | 0.0001 | 0.9999 | 0.9998 | 0.9999 | 0.9999 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3