metadata
license: apache-2.0
base_model: distilbert-base-uncased
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: 0.9269461077844311
- name: Recall
type: recall
value: 0.9381818181818182
- name: F1
type: f1
value: 0.9325301204819277
- name: Accuracy
type: accuracy
value: 0.9986404599129894
my_awesome_wnut_model
This model is a fine-tuned version of distilbert-base-uncased on the ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.0067
- Precision: 0.9269
- Recall: 0.9382
- F1: 0.9325
- Accuracy: 0.9986
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 63 | 0.0500 | 0.8048 | 0.4097 | 0.5430 | 0.9883 |
| No log | 2.0 | 126 | 0.0305 | 0.8104 | 0.7564 | 0.7824 | 0.9936 |
| No log | 3.0 | 189 | 0.0136 | 0.8643 | 0.8412 | 0.8526 | 0.9965 |
| No log | 4.0 | 252 | 0.0089 | 0.8571 | 0.9164 | 0.8858 | 0.9976 |
| No log | 5.0 | 315 | 0.0067 | 0.9269 | 0.9382 | 0.9325 | 0.9986 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3