metadata
license: mit
base_model: microsoft/llmlingua-2-xlm-roberta-large-meetingbank
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
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: 0.9570808283233133
- name: Recall
type: recall
value: 0.9644900706832716
- name: F1
type: f1
value: 0.9607711651299247
- name: Accuracy
type: accuracy
value: 0.9922812683901517
bert-finetuned-ner
This model is a fine-tuned version of microsoft/llmlingua-2-xlm-roberta-large-meetingbank on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0434
- Precision: 0.9571
- Recall: 0.9645
- F1: 0.9608
- Accuracy: 0.9923
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 |
|---|---|---|---|---|---|---|---|
| 0.0716 | 1.0 | 1756 | 0.0592 | 0.9321 | 0.9468 | 0.9394 | 0.9885 |
| 0.0344 | 2.0 | 3512 | 0.0518 | 0.9507 | 0.9581 | 0.9544 | 0.9908 |
| 0.0213 | 3.0 | 5268 | 0.0434 | 0.9571 | 0.9645 | 0.9608 | 0.9923 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2