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---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilBERT-infoExtract
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: 0.9133716160787531
- name: Recall
type: recall
value: 0.9368899360484685
- name: F1
type: f1
value: 0.9249813076347928
- name: Accuracy
type: accuracy
value: 0.9832077471007241
language:
- en
---
<!-- 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-infoExtract
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0718
- Precision: 0.9134
- Recall: 0.9369
- F1: 0.9250
- Accuracy: 0.9832
## Model description
The model can identify human name, organization and location so far (no time recognition). It was trained for 5 minutes with T4 GPU on Colab.
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0954 | 1.0 | 1756 | 0.0846 | 0.8880 | 0.9194 | 0.9034 | 0.9769 |
| 0.0498 | 2.0 | 3512 | 0.0699 | 0.9057 | 0.9310 | 0.9182 | 0.9815 |
| 0.031 | 3.0 | 5268 | 0.0718 | 0.9134 | 0.9369 | 0.9250 | 0.9832 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1 |