metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- fin
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: fin
type: fin
config: fin
split: validation
args: fin
metrics:
- name: Precision
type: precision
value: 0.9825072886297376
- name: Recall
type: recall
value: 0.8776041666666666
- name: F1
type: f1
value: 0.9270976616231086
- name: Accuracy
type: accuracy
value: 0.9851503078594712
distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the fin dataset. It achieves the following results on the evaluation set:
- Loss: 0.1142
- Precision: 0.9825
- Recall: 0.8776
- F1: 0.9271
- Accuracy: 0.9852
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: 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 64 | 0.1589 | 0.9760 | 0.7422 | 0.8432 | 0.9752 |
| No log | 2.0 | 128 | 0.1221 | 0.9731 | 0.7526 | 0.8488 | 0.9765 |
| No log | 3.0 | 192 | 0.1142 | 0.9825 | 0.8776 | 0.9271 | 0.9852 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1