README.md

---
license: apache-2.0
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
pipeline_tag: token-classification
widget:
- text: "here, da = direct assessment, rr = relative ranking, ds = discrete scale and cs = continuous scale."
  example_title: "Uncased 1"
- text: "modifying or replacing the erasable programmable read only memory (eprom) in a phone would allow the configuration of any esn and min via software for cellular devices."
  example_title: "Uncased 2"
- text: "we propose a technique called aggressive stochastic weight averaging (aswa) and an extension called norm-filtered aggressive stochastic weight averaging (naswa) which improves te stability of models over random seeds."
  example_title: "Uncased 3"
- text: "the choice of the encoder and decoder modules of dnpg can be quite flexible, for instance long-short term memory networks (lstm) or convolutional neural network (cnn)."
  example_title: "Uncased 4"
model-index:
  - name: SpanMarker w. bert-base-uncased on Acronym Identification by Tom Aarsen
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          type: acronym_identification
          name: Acronym Identification
          split: validation
          revision: c3c245a18bbd57b1682b099e14460eebf154cbdf
        metrics:
          - type: f1
            value: 0.9198
            name: F1
          - type: precision
            value: 0.9252
            name: Precision
          - type: recall
            value: 0.9145
            name: Recall
datasets:
  - acronym_identification
language:
  - en
metrics:
  - f1
  - recall
  - precision
---

# SpanMarker for uncased Acronyms Named Entity Recognition

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses [bert-base-uncased](https://huggingface.co/bert-base-uncased) as the underlying encoder. See [train.py](train.py) for the training script.

Is your data only capitalized correctly? Then consider using the cased variant of this model instead for better performance: 
[tomaarsen/span-marker-bert-base-acronyms](https://huggingface.co/tomaarsen/span-marker-bert-base-acronyms).

## Metrics

It achieves the following results on the validation set:
- Overall Precision: 0.9252
- Overall Recall: 0.9145
- Overall F1: 0.9198
- Overall Accuracy: 0.9797

## Labels

| **Label** | **Examples** |
|-----------|--------------|
| SHORT     | "nlp", "coqa", "soda", "sca" | 
| LONG      | "natural language processing", "conversational question answering", "symposium on discrete algorithms", "successive convex approximation" |

## Usage

To use this model for inference, first install the `span_marker` library:

```bash
pip install span_marker
```

You can then run inference with this model like so:

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-uncased-acronyms")
# Run inference
entities = model.predict("compression algorithms like principal component analysis (pca) can reduce noise and complexity.")
```

See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.013         | 0.31  | 200  | 0.0101          | 0.8998            | 0.8514         | 0.8749     | 0.9696           |
| 0.0088        | 0.62  | 400  | 0.0082          | 0.8997            | 0.9142         | 0.9069     | 0.9764           |
| 0.0082        | 0.94  | 600  | 0.0071          | 0.9173            | 0.8955         | 0.9063     | 0.9765           |
| 0.0063        | 1.25  | 800  | 0.0066          | 0.9210            | 0.9187         | 0.9198     | 0.9802           |
| 0.0066        | 1.56  | 1000 | 0.0066          | 0.9302            | 0.8941         | 0.9118     | 0.9783           |
| 0.0064        | 1.87  | 1200 | 0.0063          | 0.9304            | 0.9042         | 0.9171     | 0.9792           |
| 0.0063        | 2.00  | 1290 | 0.0063          | 0.9252            | 0.9145         | 0.9198     | 0.9797           |

### Framework versions

- SpanMarker 1.2.4
- Transformers 4.31.0
- Pytorch 1.13.1+cu117
- Datasets 2.14.3
- Tokenizers 0.13.2