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---
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 |