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---
license: mit
tags:
- generated_from_trainer
datasets:
- tner/ontonotes5
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: 'I am Jack. I live in California and I work at Apple '
example_title: Example 1
- text: 'Wow this book is amazing and costs only 4€ '
example_title: Example 2
base_model: distilbert-base-cased
model-index:
- name: distilbert-finetuned-ner-ontonotes
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: ontonotes5
type: ontonotes5
config: ontonotes5
split: train
args: ontonotes5
metrics:
- type: precision
value: 0.8535359959297889
name: Precision
- type: recall
value: 0.8788553467356427
name: Recall
- type: f1
value: 0.8660106468785288
name: F1
- type: accuracy
value: 0.9749625470373822
name: Accuracy
---
<!-- 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-finetuned-ner-ontonotes
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co./distilbert-base-cased) on the ontonotes5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1448
- Precision: 0.8535
- Recall: 0.8789
- F1: 0.8660
- Accuracy: 0.9750
## Model description
Token classification experiment, NER, on business topics.
## Intended uses & limitations
The model can be used on token classification, in particular NER. It is fine tuned on business domain.
## Training and evaluation data
The dataset used is [ontonotes5](https://huggingface.co./datasets/tner/ontonotes5)
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0937 | 1.0 | 7491 | 0.0998 | 0.8367 | 0.8587 | 0.8475 | 0.9731 |
| 0.0572 | 2.0 | 14982 | 0.1084 | 0.8338 | 0.8759 | 0.8543 | 0.9737 |
| 0.0403 | 3.0 | 22473 | 0.1145 | 0.8521 | 0.8707 | 0.8613 | 0.9748 |
| 0.0265 | 4.0 | 29964 | 0.1222 | 0.8535 | 0.8815 | 0.8672 | 0.9752 |
| 0.0148 | 5.0 | 37455 | 0.1365 | 0.8536 | 0.8770 | 0.8651 | 0.9747 |
| 0.0111 | 6.0 | 44946 | 0.1448 | 0.8535 | 0.8789 | 0.8660 | 0.9750 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
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