|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- mit_restaurant |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: distilbert-finetuned-mit-restaurant-ner |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: mit_restaurant |
|
type: mit_restaurant |
|
config: mit_restaurant |
|
split: validation |
|
args: mit_restaurant |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.776800439802089 |
|
- name: Recall |
|
type: recall |
|
value: 0.7983050847457627 |
|
- name: F1 |
|
type: f1 |
|
value: 0.7874059626636947 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9116093286947559 |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# distilbert-finetuned-mit-restaurant-ner |
|
|
|
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the mit_restaurant dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3210 |
|
- Precision: 0.7768 |
|
- Recall: 0.7983 |
|
- F1: 0.7874 |
|
- Accuracy: 0.9116 |
|
|
|
## 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: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.6991 | 1.0 | 863 | 0.3478 | 0.7113 | 0.7684 | 0.7387 | 0.8994 | |
|
| 0.2773 | 2.0 | 1726 | 0.3264 | 0.7533 | 0.7989 | 0.7754 | 0.9063 | |
|
| 0.2164 | 3.0 | 2589 | 0.3137 | 0.7644 | 0.8045 | 0.7839 | 0.9121 | |
|
| 0.1789 | 4.0 | 3452 | 0.3163 | 0.7755 | 0.7983 | 0.7867 | 0.9115 | |
|
| 0.1573 | 5.0 | 4315 | 0.3210 | 0.7768 | 0.7983 | 0.7874 | 0.9116 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.26.1 |
|
- Pytorch 1.13.1+cu116 |
|
- Datasets 2.9.0 |
|
- Tokenizers 0.13.2 |
|
|
