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---
license: apache-2.0
base_model: dslim/distilbert-NER
tags:
- generated_from_trainer
datasets:
- conll2012_ontonotesv5
metrics:
- f1
model-index:
- name: distilbert-NER-finetuned-bert-tiny
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2012_ontonotesv5
type: conll2012_ontonotesv5
config: english_v4
split: validation
args: english_v4
metrics:
- name: F1
type: f1
value: 0.4683072334079046
---
<!-- 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. -->
# distilbert-NER-finetuned-bert-tiny
This model is a fine-tuned version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER) on the conll2012_ontonotesv5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5602
- F1: 0.4683
## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0495 | 1.0 | 81 | 0.7833 | 0.3529 |
| 0.6421 | 2.0 | 162 | 0.6088 | 0.4570 |
| 0.5065 | 3.0 | 243 | 0.5602 | 0.4683 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1