---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilBert_NER_finer
  results: []
datasets:
- nlpaueb/finer-139
language:
- en
pipeline_tag: token-classification
---

<!-- 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_finer

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the [Finer-139](https://huggingface.co/datasets/nlpaueb/finer-139) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0198
- Precision: 0.9445
- Recall: 0.9640
- F1: 0.9541
- Accuracy: 0.9954


## Training and evaluation data

The training data consists of the 4 most widely available ner_tags from the Finer-139 dataset. The training and the test data were curated from this source accordingly

## Prediction procedure
```
from transformers import TAutoTokenizer
from optimum.onnxruntime import ORTModelForTokenClassification
import torch

def onnx_inference(checkpoint, test_data, export=False):
  test_text = " ".join(test_data['tokens'])
  print("Test Text: " + test_text)

  tokenizer = AutoTokenizer.from_pretrained(checkpoint)
  model = ORTModelForTokenClassification.from_pretrained(checkpoint, export=export)

  inputs = tokenizer(test_text, return_tensors="pt")
  outputs = model(**inputs).logits

  predictions = torch.argmax(outputs, dim=2)

  # Convert each tensor element to a scalar before calling .item()
  predicted_token_class = [label_list[int(t)] for t in predictions[0]]
  ner_tags = [label_list[int(t)] for t in test_data['ner_tags']]

  print("Original Tags: ")
  print(ner_tags)
  print("Predicted Tags: ")
  print(predicted_token_class)

onnx_model_path = "" #add the path

onnx_inference(onnx_model_path, test_data)

"""
Here the test_data should contain "tokens" and "ner_tags". This can be of type Dataset.
"""

```
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0034        | 1.0   | 1620 | 0.0261          | 0.9167    | 0.9668 | 0.9411 | 0.9941   |
| 0.0031        | 2.0   | 3240 | 0.0182          | 0.9471    | 0.9651 | 0.9561 | 0.9956   |
| 0.0012        | 3.0   | 4860 | 0.0198          | 0.9445    | 0.9640 | 0.9541 | 0.9954   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2