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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: "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.
## 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
|