metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: products-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ner
type: ner
config: ner
split: test
args: ner
metrics:
- name: Precision
type: precision
value: 0.8186813186813187
- name: Recall
type: recall
value: 0.8563218390804598
- name: F1
type: f1
value: 0.8370786516853932
- name: Accuracy
type: accuracy
value: 0.9532710280373832
products-ner
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: 0.1747
- Precision: 0.8187
- Recall: 0.8563
- F1: 0.8371
- Accuracy: 0.9533
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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 30 | 0.7395 | 0.3421 | 0.3736 | 0.3571 | 0.7897 |
| No log | 2.0 | 60 | 0.4036 | 0.5842 | 0.6782 | 0.6277 | 0.8863 |
| No log | 3.0 | 90 | 0.2716 | 0.7105 | 0.7759 | 0.7418 | 0.9174 |
| No log | 4.0 | 120 | 0.2286 | 0.7433 | 0.7989 | 0.7701 | 0.9315 |
| No log | 5.0 | 150 | 0.2093 | 0.7760 | 0.8161 | 0.7955 | 0.9377 |
| No log | 6.0 | 180 | 0.1890 | 0.7796 | 0.8333 | 0.8056 | 0.9455 |
| No log | 7.0 | 210 | 0.1772 | 0.8197 | 0.8621 | 0.8403 | 0.9533 |
| No log | 8.0 | 240 | 0.1747 | 0.8187 | 0.8563 | 0.8371 | 0.9533 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1