metadata
base_model: tner/roberta-base-tweetner7-2021
tags:
- generated_from_trainer
datasets:
- tweetner7
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-semantic-augmentation-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tweetner7
type: tweetner7
config: tweetner7
split: validation_2021
args: tweetner7
metrics:
- name: Precision
type: precision
value: 0.7156323644933229
- name: Recall
type: recall
value: 0.7214889123548046
- name: F1
type: f1
value: 0.7185487051400026
- name: Accuracy
type: accuracy
value: 0.8840362386754139
bert-finetuned-semantic-augmentation-ner
This model is a fine-tuned version of tner/roberta-base-tweetner7-2021 on the tweetner7 dataset. It achieves the following results on the evaluation set:
- Loss: 0.7890
- Precision: 0.7156
- Recall: 0.7215
- F1: 0.7185
- Accuracy: 0.8840
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 312 | 0.7153 | 0.7074 | 0.7072 | 0.7073 | 0.8823 |
| 0.0508 | 2.0 | 624 | 0.7532 | 0.7196 | 0.7215 | 0.7205 | 0.8861 |
| 0.0508 | 3.0 | 936 | 0.7890 | 0.7156 | 0.7215 | 0.7185 | 0.8840 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3