|
---
|
|
license: apache-2.0
|
|
base_model: distilroberta-base
|
|
tags:
|
|
- generated_from_trainer
|
|
datasets:
|
|
- conll2003
|
|
metrics:
|
|
- precision
|
|
- recall
|
|
- f1
|
|
- accuracy
|
|
model-index:
|
|
- name: RoBERTa_conll_learning_rate7e5
|
|
results:
|
|
- task:
|
|
name: Token Classification
|
|
type: token-classification
|
|
dataset:
|
|
name: conll2003
|
|
type: conll2003
|
|
config: conll2003
|
|
split: validation
|
|
args: conll2003
|
|
metrics:
|
|
- name: Precision
|
|
type: precision
|
|
value: 0.9373342175066313
|
|
- name: Recall
|
|
type: recall
|
|
value: 0.9515314708852238
|
|
- name: F1
|
|
type: f1
|
|
value: 0.9443794888926006
|
|
- name: Accuracy
|
|
type: accuracy
|
|
value: 0.9872203982694608
|
|
---
|
|
|
|
<!-- 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. -->
|
|
|
|
# RoBERTa_conll_learning_rate7e5
|
|
|
|
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the conll2003 dataset.
|
|
It achieves the following results on the evaluation set:
|
|
- Loss: 0.0592
|
|
- Precision: 0.9373
|
|
- Recall: 0.9515
|
|
- F1: 0.9444
|
|
- Accuracy: 0.9872
|
|
|
|
## 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: 7e-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: 3
|
|
|
|
### Training results
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
|
|
| 0.0822 | 1.0 | 1756 | 0.0665 | 0.9087 | 0.9349 | 0.9216 | 0.9821 |
|
|
| 0.0329 | 2.0 | 3512 | 0.0644 | 0.9340 | 0.9429 | 0.9384 | 0.9860 |
|
|
| 0.0205 | 3.0 | 5268 | 0.0592 | 0.9373 | 0.9515 | 0.9444 | 0.9872 |
|
|
|
|
|
|
### Framework versions
|
|
|
|
- Transformers 4.41.2
|
|
- Pytorch 2.3.1+cu121
|
|
- Datasets 2.20.0
|
|
- Tokenizers 0.19.1
|
|
|
