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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
- autoevaluate/conll2003-sample
metrics:
- precision
- recall
- f1
- accuracy
duplicated_from: autoevaluate/entity-extraction
model-index:
- name: autoevaluate/entity-extraction-not-evaluated
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: autoevaluate/conll2003-sample
type: autoevaluate/conll2003-sample
config: autoevaluate--conll2003-sample
split: test
metrics:
- type: accuracy
value: 0.9680247550283652
name: Accuracy
verified: true
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- type: precision
value: 0.9708377518557795
name: Precision
verified: true
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- type: recall
value: 0.9754928076718167
name: Recall
verified: true
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- type: auc
value: NaN
name: AUC
verified: true
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- type: f1
value: 0.9731597129949509
name: F1
verified: true
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- type: loss
value: 0.1187286302447319
name: loss
verified: true
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---
<!-- 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. -->
# entity-extraction
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0808
- Precision: 0.8863
- Recall: 0.9085
- F1: 0.8972
- Accuracy: 0.9775
## 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: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2552 | 1.0 | 878 | 0.0808 | 0.8863 | 0.9085 | 0.8972 | 0.9775 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|