license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-FiNER
results: []
datasets:
- nlpaueb/finer-139
language:
- en
pipeline_tag: token-classification
distilbert-base-uncased-finetuned-FiNER
This model is a fine-tuned version of distilbert/distilbert-base-uncased trained on a subset of the nlpaueb/finer-139 dataset. The subset is generated by filtering the dataset to contain only samples with at least one of the following NER tags:
- 'O',
- 'B-DebtInstrumentBasisSpreadOnVariableRate1',
- 'B-DebtInstrumentFaceAmount',
- 'B-LineOfCreditFacilityMaximumBorrowingCapacity',
- 'B-DebtInstrumentInterestRateStatedPercentage'
Then, it was fine-tuned to detect only the afforementioned 4 tags (plus other "O")
It achieves the following results on the evaluation set:
- Loss: 0.0336
- Precision: 0.9154
- Recall: 0.9327
- F1: 0.9240
- Accuracy: 0.9917
Model description
Model based on distilbert/distilbert-base-uncased with all default parameters.
Intended uses & limitations
The model published here was trained for demo purposes only.
Training and evaluation data
Original train/validation/test splits from nlpaueb/finer-139, after filtering for samples containing at least one of the following NER tags:
- 'O',
- 'B-DebtInstrumentBasisSpreadOnVariableRate1',
- 'B-DebtInstrumentFaceAmount',
- 'B-LineOfCreditFacilityMaximumBorrowingCapacity',
- 'B-DebtInstrumentInterestRateStatedPercentage'
Training procedure
Follow information here https://github.com/bodias/DistilBERT-FiNER
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0354 | 1.0 | 1773 | 0.0375 | 0.8639 | 0.8993 | 0.8812 | 0.9870 |
| 0.0242 | 2.0 | 3546 | 0.0296 | 0.8929 | 0.9159 | 0.9042 | 0.9895 |
| 0.0166 | 3.0 | 5319 | 0.0297 | 0.9079 | 0.9208 | 0.9143 | 0.9907 |
| 0.0117 | 4.0 | 7092 | 0.0303 | 0.9101 | 0.9293 | 0.9196 | 0.9913 |
| 0.0086 | 5.0 | 8865 | 0.0328 | 0.9065 | 0.9331 | 0.9196 | 0.9913 |
| 0.0062 | 6.0 | 10638 | 0.0336 | 0.9154 | 0.9327 | 0.9240 | 0.9917 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2