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: "Example 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: "Example 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 the stability of models over random seeds."
  example_title: "Example 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: "Example 4"
model-index:
  - name: SpanMarker w. bert-base-cased 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: 0.9310
            name: F1
          - type: precision
            value: 0.9423
            name: Precision
          - type: recall
            value: 0.9199
            name: Recall
datasets:
  - acronym_identification
language:
  - en
metrics:
  - f1
  - recall
  - precision
---

# SpanMarker for Acronyms Named Entity Recognition

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [acronym_identification](https://huggingface.co/datasets/acronym_identification) dataset. In particular, this SpanMarker model uses [bert-base-cased](https://huggingface.co/bert-base-cased) as the underlying encoder. See [train.py](train.py) for the training script.

Is your data not (always) capitalized correctly? Then consider using the uncased variant of this model instead for better performance: 
[tomaarsen/span-marker-bert-base-uncased-acronyms](https://huggingface.co/tomaarsen/span-marker-bert-base-uncased-acronyms).

## Metrics

It achieves the following results on the validation set:
- Overall Precision: 0.9423
- Overall Recall: 0.9199
- Overall F1: 0.9310
- Overall Accuracy: 0.9830

## 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-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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0109        | 0.31  | 200  | 0.0079          | 0.9202            | 0.8962         | 0.9080     | 0.9765           |
| 0.0075        | 0.62  | 400  | 0.0070          | 0.9358            | 0.8724         | 0.9030     | 0.9765           |
| 0.0068        | 0.93  | 600  | 0.0059          | 0.9363            | 0.9203         | 0.9282     | 0.9821           |
| 0.0057        | 1.24  | 800  | 0.0056          | 0.9372            | 0.9187         | 0.9278     | 0.9824           |
| 0.0051        | 1.55  | 1000 | 0.0054          | 0.9381            | 0.9170         | 0.9274     | 0.9824           |
| 0.0054        | 1.86  | 1200 | 0.0053          | 0.9424            | 0.9218         | 0.9320     | 0.9834           |
| 0.0054        | 2.00  | 1290 | 0.0054          | 0.9423            | 0.9199         | 0.9310     | 0.9830           |

### Framework versions

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