---
language:
- en
license: apache-2.0
library_name: span-marker
tags:
- token-classification
- named-enity-recognition
datasets:
- DFKI-SLT/few-nerd
pipeline_tag: token-classification
base_model: roberta-base
model-index:
- name: span-marker-robert-base
  results: []
---

<!-- 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. -->

# span-marker-robert-base

This model is a fine-tuned version of [roberta-base](https://huggingface.co./roberta-base) on [few-nerd](https://huggingface.co./datasets/DFKI-SLT/few-nerd) dataset using [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) an module for NER.

# Usage

```python
  from span_marker import SpanMarkerModel
  
  model = SpanMarkerModel.from_pretrained("krinal/span-marker-robert-base")
  
  ner_result = model.predict("Argentine captain Lionel Messi won Golden Ball at FIFA world cup 2022")
```

## Training and evaluation data

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Evaluation

It achieves the following results on the evaluation set:
- Loss: 0.0214
- Overall Precision: 0.7642
- Overall Recall: 0.7947
- Overall F1: 0.7791
- Overall Accuracy: 0.9397
  
### Training results

| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0214        | 0.08  | 100  | 0.0219          | 0.7641            | 0.7679         | 0.7660     | 0.9330           |
| 0.0199        | 0.16  | 200  | 0.0243          | 0.7442            | 0.7679         | 0.7559     | 0.9348           |
| 0.0179        | 0.24  | 300  | 0.0212          | 0.7730            | 0.7580         | 0.7654     | 0.9361           |
| 0.0188        | 0.33  | 400  | 0.0225          | 0.7616            | 0.7710         | 0.7662     | 0.9343           |
| 0.0149        | 0.41  | 500  | 0.0240          | 0.7537            | 0.7783         | 0.7658     | 0.9375           |
| 0.015         | 0.49  | 600  | 0.0230          | 0.7540            | 0.7829         | 0.7682     | 0.9362           |
| 0.0137        | 0.57  | 700  | 0.0232          | 0.7746            | 0.7538         | 0.7640     | 0.9319           |
| 0.0123        | 0.65  | 800  | 0.0218          | 0.7651            | 0.7879         | 0.7763     | 0.9393           |
| 0.0103        | 0.73  | 900  | 0.0223          | 0.7688            | 0.7964         | 0.7824     | 0.9397           |
| 0.0108        | 0.82  | 1000 | 0.0209          | 0.7763            | 0.7816         | 0.7789     | 0.9397           |
| 0.0116        | 0.9   | 1100 | 0.0213          | 0.7743            | 0.7879         | 0.7811     | 0.9398           |
| 0.0119        | 0.98  | 1200 | 0.0214          | 0.7653            | 0.7947         | 0.7797     | 0.9400           |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- span-marker 1.2.3