span-marker pytorch tensorboard token-classification ner named-entity-recognition generated_from_span_marker_trainer en dataset:midas/inspec license:apache-2.0 model-index co2_eq_emissions region:us
---
datasets:
- midas/inspec
language: en
library_name: span-marker
license: apache-2.0
metrics:
- precision
- recall
- f1
pipeline_tag: token-classification
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
widget:
- text: 'Genetic algorithm guided selection: variable selection and subset selection
    A novel genetic algorithm guided selection method, GAS, has been described. The
    method utilizes a simple encoding scheme which can represent both compounds and
    variables used to construct a QSAR/QSPR model. A genetic algorithm is then utilized
    to simultaneously optimize the encoded variables that include both descriptors
    and compound subsets. The GAS method generates multiple models each applying to
    a subset of the compounds. Typically the subsets represent clusters with different
    chemotypes. Also a procedure based on molecular similarity is presented to determine
    which model should be applied to a given test set compound. The variable selection
    method implemented in GAS has been tested and compared using the Selwood data
    set -LRB- n = 31 compounds; nu = 53 descriptors -RRB-. The results showed that
    the method is comparable to other published methods. The subset selection method
    implemented in GAS has been first tested using an artificial data set -LRB- n
    = 100 points; nu = 1 descriptor -RRB- to examine its ability to subset data points
    and second applied to analyze the XLOGP data set -LRB- n = 1831 compounds; nu
    = 126 descriptors -RRB-. The method is able to correctly identify artificial data
    points belonging to various subsets. The analysis of the XLOGP data set shows
    that the subset selection method can be useful in improving a QSAR/QSPR model
    when the variable selection method fails'
- text: Presentation media, information complexity, and learning outcomes Multimedia
    computing provides a variety of information presentation modality combinations.
    Educators have observed that visuals enhance learning which suggests that multimedia
    presentations should be superior to text-only and text with static pictures in
    facilitating optimal human information processing and, therefore, comprehension.
    The article reports the findings from a 3 -LRB- text-only, overhead slides, and
    multimedia presentation -RRB- * 2 -LRB- high and low information complexity -RRB-
    factorial experiment. Subjects read a text script, viewed an acetate overhead
    slide presentation, or viewed a multimedia presentation depicting the greenhouse
    effect -LRB- low complexity -RRB- or photocopier operation -LRB- high complexity
    -RRB-. Multimedia was superior to text-only and overhead slides for comprehension.
    Information complexity diminished comprehension and perceived presentation quality.
    Multimedia was able to reduce the negative impact of information complexity on
    comprehension and increase the extent of sustained attention to the presentation.
    These findings suggest that multimedia presentations invoke the use of both the
    verbal and visual working memory channels resulting in a reduction of the cognitive
    load imposed by increased information complexity. Moreover, multimedia superiority
    in facilitating comprehension goes beyond its ability to increase sustained attention;
    the quality and effectiveness of information processing attained -LRB- i.e., use
    of verbal and visual working memory -RRB- is also significant
- text: Adaptive filtering for noise reduction in hue saturation intensity color space
    Even though the hue saturation intensity -LRB- HSI -RRB- color model has been
    widely used in color image processing and analysis, the conversion formulas from
    the RGB color model to HSI are nonlinear and complicated in comparison with the
    conversion formulas of other color models. When an RGB image is degraded by random
    Gaussian noise, this nonlinearity leads to a nonuniform noise distribution in
    HSI, making accurate image analysis more difficult. We have analyzed the noise
    characteristics of the HSI color model and developed an adaptive spatial filtering
    method to reduce the magnitude of noise and the nonuniformity of noise variance
    in the HSI color space. With this adaptive filtering method, the filter kernel
    for each pixel is dynamically adjusted, depending on the values of intensity and
    saturation. In our experiments we have filtered the saturation and hue components
    and generated edge maps from color gradients. We have found that by using the
    adaptive filtering method, the minimum error rate in edge detection improves by
    approximately 15%
- text: Restoration of broadband imagery steered with a liquid-crystal optical phased
    array In many imaging applications, it is highly desirable to replace mechanical
    beam-steering components -LRB- i.e., mirrors and gimbals -RRB- with a nonmechanical
    device. One such device is a nematic liquid crystal optical phased array -LRB-
    LCOPA -RRB-. An LCOPA can implement a blazed phase grating to steer the incident
    light. However, when a phase grating is used in a broadband imaging system, two
    adverse effects can occur. First, dispersion will cause different incident wavelengths
    arriving at the same angle to be steered to different output angles, causing chromatic
    aberrations in the image plane. Second, the device will steer energy not only
    to the first diffraction order, but to others as well. This multiple-order effect
    results in multiple copies of the scene appearing in the image plane. We describe
    a digital image restoration technique designed to overcome these degradations.
    The proposed postprocessing technique is based on a Wiener deconvolution filter.
    The technique, however, is applicable only to scenes containing objects with approximately
    constant reflectivities over the spectral region of interest. Experimental results
    are presented to demonstrate the effectiveness of this technique
- text: A comparison of computational color constancy Algorithms. II. Experiments
    with image data For pt.I see ibid., vol. 11, no. 9, p.972-84 -LRB- 2002 -RRB-.
    We test a number of the leading computational color constancy algorithms using
    a comprehensive set of images. These were of 33 different scenes under 11 different
    sources representative of common illumination conditions. The algorithms studied
    include two gray world methods, a version of the Retinex method, several variants
    of Forsyth's -LRB- 1990 -RRB- gamut-mapping method, Cardei et al.'s -LRB- 2000
    -RRB- neural net method, and Finlayson et al.'s color by correlation method -LRB-
    Finlayson et al. 1997, 2001; Hubel and Finlayson 2000 -RRB-. We discuss a number
    of issues in applying color constancy ideas to image data, and study in depth
    the effect of different preprocessing strategies. We compare the performance of
    the algorithms on image data with their performance on synthesized data. All data
    used for this study are available online at http://www.cs.sfu.ca/~color/data,
    and implementations for most of the algorithms are also available -LRB- http://www.cs.sfu.ca/~color/code
    -RRB-. Experiments with synthesized data -LRB- part one of this paper -RRB- suggested
    that the methods which emphasize the use of the input data statistics, specifically
    color by correlation and the neural net algorithm, are potentially the most effective
    at estimating the chromaticity of the scene illuminant. Unfortunately, we were
    unable to realize comparable performance on real images. Here exploiting pixel
    intensity proved to be more beneficial than exploiting the details of image chromaticity
    statistics, and the three-dimensional -LRB- 3-D -RRB- gamut-mapping algorithms
    gave the best performance
co2_eq_emissions:
  emissions: 20.795
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  gpu_model: 1 x NVIDIA GeForce RTX 3090
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.137
model-index:
- name: SpanMarker with bert-base-uncased on Inspec
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: Inspec
      type: midas/inspec
      split: test
    metrics:
    - type: f1
      value: 0.5934525191548642
      name: F1
    - type: precision
      value: 0.5666149412547107
      name: Precision
    - type: recall
      value: 0.6229588106263709
      name: Recall
---

# SpanMarker with bert-base-uncased on Inspec

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [Inspec](https://huggingface.co/datasets/midas/inspec) dataset that can be used for Named Entity Recognition. 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.

## Model Details

### Model Description

- **Model Type:** SpanMarker
- **Encoder:** [bert-base-uncased](https://huggingface.co/bert-base-uncased)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [Inspec](https://huggingface.co/datasets/midas/inspec)
- **Language:** en
- **License:** apache-2.0

### Model Sources

- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)

### Model Labels
| Label | Examples                                       |
|:------|:-----------------------------------------------|
| KEY   | "Content Atomism", "philosophy of mind", "IBS" |

## Evaluation

### Metrics
| Label   | Precision | Recall | F1     |
|:--------|:----------|:-------|:-------|
| **all** | 0.5666    | 0.6230 | 0.5935 |
| KEY     | 0.5666    | 0.6230 | 0.5935 |

## Uses

### Direct Use

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-uncased-keyphrase-inspec")
# Run inference
entities = model.predict("Adaptive filtering for noise reduction in hue saturation intensity color space Even though the hue saturation intensity -LRB- HSI -RRB- color model has been widely used in color image processing and analysis, the conversion formulas from the RGB color model to HSI are nonlinear and complicated in comparison with the conversion formulas of other color models. When an RGB image is degraded by random Gaussian noise, this nonlinearity leads to a nonuniform noise distribution in HSI, making accurate image analysis more difficult. We have analyzed the noise characteristics of the HSI color model and developed an adaptive spatial filtering method to reduce the magnitude of noise and the nonuniformity of noise variance in the HSI color space. With this adaptive filtering method, the filter kernel for each pixel is dynamically adjusted, depending on the values of intensity and saturation. In our experiments we have filtered the saturation and hue components and generated edge maps from color gradients. We have found that by using the adaptive filtering method, the minimum error rate in edge detection improves by approximately 15%")
```

### Downstream Use
You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

```python
from span_marker import SpanMarkerModel, Trainer

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-uncased-keyphrase-inspec")

# Specify a Dataset with "tokens" and "ner_tag" columns
dataset = load_dataset("conll2003") # For example CoNLL2003

# Initialize a Trainer using the pretrained model & dataset
trainer = Trainer(
    model=model,
    train_dataset=dataset["train"],
    eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("tomaarsen/span-marker-bert-base-uncased-keyphrase-inspec-finetuned")
```
</details>

## Training Details

### Training Set Metrics
| Training set          | Min | Median   | Max |
|:----------------------|:----|:---------|:----|
| Sentence length       | 15  | 138.5327 | 557 |
| Entities per sentence | 0   | 8.2507   | 41  |

### Training Hyperparameters
- 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: 3

### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.021 kg of CO2
- **Hours Used**: 0.137 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB

### Framework Versions

- Python: 3.9.16
- SpanMarker: 1.3.1.dev
- Transformers : 4.29.2
- PyTorch: 2.0.1+cu118
- Datasets: 2.14.3
- Tokenizers: 0.13.2