|
|
---
|
|
|
library_name: transformers
|
|
|
license: apache-2.0
|
|
|
base_model: distilbert/distilbert-base-uncased
|
|
|
tags:
|
|
|
- generated_from_trainer
|
|
|
datasets:
|
|
|
- wnut_17
|
|
|
metrics:
|
|
|
- precision
|
|
|
- recall
|
|
|
- f1
|
|
|
- accuracy
|
|
|
model-index:
|
|
|
- name: my_awesome_wnut_model
|
|
|
results:
|
|
|
- task:
|
|
|
name: Token Classification
|
|
|
type: token-classification
|
|
|
dataset:
|
|
|
name: wnut_17
|
|
|
type: wnut_17
|
|
|
config: wnut_17
|
|
|
split: test
|
|
|
args: wnut_17
|
|
|
metrics:
|
|
|
- name: Precision
|
|
|
type: precision
|
|
|
value: 0.5718901453957996
|
|
|
- name: Recall
|
|
|
type: recall
|
|
|
value: 0.32808155699721964
|
|
|
- name: F1
|
|
|
type: f1
|
|
|
value: 0.4169611307420495
|
|
|
- name: Accuracy
|
|
|
type: accuracy
|
|
|
value: 0.9421144884784747
|
|
|
---
|
|
|
|
|
|
<!-- 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. -->
|
|
|
|
|
|
# my_awesome_wnut_model
|
|
|
|
|
|
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the wnut_17 dataset.
|
|
|
It achieves the following results on the evaluation set:
|
|
|
- Loss: 0.2706
|
|
|
- Precision: 0.5719
|
|
|
- Recall: 0.3281
|
|
|
- F1: 0.4170
|
|
|
- Accuracy: 0.9421
|
|
|
|
|
|
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
|
|
|
- lr_scheduler_type: linear
|
|
|
- num_epochs: 2
|
|
|
|
|
|
### Training results
|
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|
|
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
|
|
|
| No log | 1.0 | 213 | 0.2857 | 0.5354 | 0.2382 | 0.3297 | 0.9380 |
|
|
|
| No log | 2.0 | 426 | 0.2706 | 0.5719 | 0.3281 | 0.4170 | 0.9421 |
|
|
|
|
|
|
|
|
|
### Framework versions
|
|
|
|
|
|
- Transformers 4.56.2
|
|
|
- Pytorch 2.5.1+cu121
|
|
|
- Datasets 3.6.0
|
|
|
- Tokenizers 0.22.1
|
|
|
|
