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---
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
|