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--- |
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language: en |
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license: apache-2.0 |
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library_name: span-marker |
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tags: |
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- span-marker |
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- token-classification |
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- ner |
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- named-entity-recognition |
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- generated_from_span_marker_trainer |
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datasets: |
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- midas/inspec |
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metrics: |
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- precision |
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- recall |
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- f1 |
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widget: |
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- text: 'Genetic algorithm guided selection: variable selection and subset selection |
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A novel genetic algorithm guided selection method, GAS, has been described. |
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The method utilizes a simple encoding scheme which can represent both compounds |
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and variables used to construct a QSAR/QSPR model. A genetic algorithm is then |
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utilized to simultaneously optimize the encoded variables that include both descriptors |
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and compound subsets. The GAS method generates multiple models each applying |
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to a subset of the compounds. Typically the subsets represent clusters with different |
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chemotypes. Also a procedure based on molecular similarity is presented to determine |
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which model should be applied to a given test set compound. The variable selection |
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method implemented in GAS has been tested and compared using the Selwood data |
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set -LRB- n = 31 compounds; nu = 53 descriptors -RRB-. The results showed that |
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the method is comparable to other published methods. The subset selection method |
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implemented in GAS has been first tested using an artificial data set -LRB- n |
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= 100 points; nu = 1 descriptor -RRB- to examine its ability to subset data points |
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and second applied to analyze the XLOGP data set -LRB- n = 1831 compounds; nu |
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= 126 descriptors -RRB-. The method is able to correctly identify artificial |
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data points belonging to various subsets. The analysis of the XLOGP data set |
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shows that the subset selection method can be useful in improving a QSAR/QSPR |
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model when the variable selection method fails' |
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- text: Presentation media, information complexity, and learning outcomes Multimedia |
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computing provides a variety of information presentation modality combinations. |
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Educators have observed that visuals enhance learning which suggests that multimedia |
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presentations should be superior to text-only and text with static pictures in |
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facilitating optimal human information processing and, therefore, comprehension. |
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The article reports the findings from a 3 -LRB- text-only, overhead slides, |
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and multimedia presentation -RRB- * 2 -LRB- high and low information complexity |
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-RRB- factorial experiment. Subjects read a text script, viewed an acetate overhead |
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slide presentation, or viewed a multimedia presentation depicting the greenhouse |
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effect -LRB- low complexity -RRB- or photocopier operation -LRB- high complexity |
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-RRB-. Multimedia was superior to text-only and overhead slides for comprehension. |
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Information complexity diminished comprehension and perceived presentation quality. |
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Multimedia was able to reduce the negative impact of information complexity |
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on comprehension and increase the extent of sustained attention to the presentation. |
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These findings suggest that multimedia presentations invoke the use of both |
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the verbal and visual working memory channels resulting in a reduction of the |
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cognitive load imposed by increased information complexity. Moreover, multimedia |
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superiority in facilitating comprehension goes beyond its ability to increase |
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sustained attention; the quality and effectiveness of information processing |
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attained -LRB- i.e., use of verbal and visual working memory -RRB- is also significant |
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- text: Adaptive filtering for noise reduction in hue saturation intensity color space |
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Even though the hue saturation intensity -LRB- HSI -RRB- color model has been |
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widely used in color image processing and analysis, the conversion formulas from |
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the RGB color model to HSI are nonlinear and complicated in comparison with the |
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conversion formulas of other color models. When an RGB image is degraded by random |
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Gaussian noise, this nonlinearity leads to a nonuniform noise distribution in |
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HSI, making accurate image analysis more difficult. We have analyzed the noise |
|
characteristics of the HSI color model and developed an adaptive spatial filtering |
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method to reduce the magnitude of noise and the nonuniformity of noise variance |
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in the HSI color space. With this adaptive filtering method, the filter kernel |
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for each pixel is dynamically adjusted, depending on the values of intensity |
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and saturation. In our experiments we have filtered the saturation and hue components |
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and generated edge maps from color gradients. We have found that by using the |
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adaptive filtering method, the minimum error rate in edge detection improves |
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by approximately 15% |
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- text: Restoration of broadband imagery steered with a liquid-crystal optical phased |
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array In many imaging applications, it is highly desirable to replace mechanical |
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beam-steering components -LRB- i.e., mirrors and gimbals -RRB- with a nonmechanical |
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device. One such device is a nematic liquid crystal optical phased array -LRB- |
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LCOPA -RRB-. An LCOPA can implement a blazed phase grating to steer the incident |
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light. However, when a phase grating is used in a broadband imaging system, |
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two adverse effects can occur. First, dispersion will cause different incident |
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wavelengths arriving at the same angle to be steered to different output angles, |
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causing chromatic aberrations in the image plane. Second, the device will |
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steer energy not only to the first diffraction order, but to others as well. |
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This multiple-order effect results in multiple copies of the scene appearing in |
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the image plane. We describe a digital image restoration technique designed to |
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overcome these degradations. The proposed postprocessing technique is based on |
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a Wiener deconvolution filter. The technique, however, is applicable only to |
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scenes containing objects with approximately constant reflectivities over the |
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spectral region of interest. Experimental results are presented to demonstrate |
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the effectiveness of this technique |
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- text: A comparison of computational color constancy Algorithms. II. Experiments |
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with image data For pt.I see ibid., vol. 11, no. 9, p.972-84 -LRB- 2002 -RRB-. |
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We test a number of the leading computational color constancy algorithms using |
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a comprehensive set of images. These were of 33 different scenes under 11 different |
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sources representative of common illumination conditions. The algorithms studied |
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include two gray world methods, a version of the Retinex method, several variants |
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of Forsyth's -LRB- 1990 -RRB- gamut-mapping method, Cardei et al.'s -LRB- 2000 |
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-RRB- neural net method, and Finlayson et al.'s color by correlation method |
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-LRB- Finlayson et al. 1997, 2001; Hubel and Finlayson 2000 -RRB-. We discuss |
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a number of issues in applying color constancy ideas to image data, and study |
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in depth the effect of different preprocessing strategies. We compare the performance |
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of the algorithms on image data with their performance on synthesized data. All |
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data used for this study are available online at http://www.cs.sfu.ca/~color/data, |
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and implementations for most of the algorithms are also available -LRB- http://www.cs.sfu.ca/~color/code |
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-RRB-. Experiments with synthesized data -LRB- part one of this paper -RRB- suggested |
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that the methods which emphasize the use of the input data statistics, specifically |
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color by correlation and the neural net algorithm, are potentially the most effective |
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at estimating the chromaticity of the scene illuminant. Unfortunately, we were |
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unable to realize comparable performance on real images. Here exploiting pixel |
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intensity proved to be more beneficial than exploiting the details of image chromaticity |
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statistics, and the three-dimensional -LRB- 3-D -RRB- gamut-mapping algorithms |
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gave the best performance |
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pipeline_tag: token-classification |
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co2_eq_emissions: |
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emissions: 20.795 |
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source: codecarbon |
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training_type: fine-tuning |
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on_cloud: false |
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gpu_model: 1 x NVIDIA GeForce RTX 3090 |
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cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K |
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ram_total_size: 31.777088165283203 |
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hours_used: 0.137 |
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model-index: |
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- name: SpanMarker with bert-base-uncased on Inspec |
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results: |
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- task: |
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type: token-classification |
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name: Named Entity Recognition |
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dataset: |
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name: Inspec |
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type: midas/inspec |
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split: test |
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metrics: |
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- type: f1 |
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value: 0.5934525191548642 |
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name: F1 |
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- type: precision |
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value: 0.5666149412547107 |
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name: Precision |
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- type: recall |
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value: 0.6229588106263709 |
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name: Recall |
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--- |
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|
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# SpanMarker with bert-base-uncased on Inspec |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** SpanMarker |
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- **Encoder:** [bert-base-uncased](https://huggingface.co/bert-base-uncased) |
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- **Maximum Sequence Length:** 256 tokens |
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- **Maximum Entity Length:** 8 words |
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- **Training Dataset:** [Inspec](https://huggingface.co/datasets/midas/inspec) |
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- **Language:** en |
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- **License:** apache-2.0 |
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### Model Sources |
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- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER) |
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- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf) |
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### Model Labels |
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| Label | Examples | |
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|:------|:-----------------------------------------------| |
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| KEY | "Content Atomism", "philosophy of mind", "IBS" | |
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## Evaluation |
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### Metrics |
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| Label | Precision | Recall | F1 | |
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|:--------|:----------|:-------|:-------| |
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| **all** | 0.5666 | 0.6230 | 0.5935 | |
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| KEY | 0.5666 | 0.6230 | 0.5935 | |
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## Uses |
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### Direct Use |
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```python |
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from span_marker import SpanMarkerModel |
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# Download from the 🤗 Hub |
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model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-uncased-keyphrase-inspec") |
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# Run inference |
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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%") |
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``` |
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### Downstream Use |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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```python |
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from span_marker import SpanMarkerModel, Trainer |
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# Download from the 🤗 Hub |
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model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-uncased-keyphrase-inspec") |
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# Specify a Dataset with "tokens" and "ner_tag" columns |
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dataset = load_dataset("conll2003") # For example CoNLL2003 |
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# Initialize a Trainer using the pretrained model & dataset |
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trainer = Trainer( |
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model=model, |
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train_dataset=dataset["train"], |
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eval_dataset=dataset["validation"], |
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) |
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trainer.train() |
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trainer.save_model("tomaarsen/span-marker-bert-base-uncased-keyphrase-inspec-finetuned") |
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``` |
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</details> |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:----------------------|:----|:---------|:----| |
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| Sentence length | 15 | 138.5327 | 557 | |
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| Entities per sentence | 0 | 8.2507 | 41 | |
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### Training Hyperparameters |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3 |
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### Environmental Impact |
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Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). |
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- **Carbon Emitted**: 0.021 kg of CO2 |
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- **Hours Used**: 0.137 hours |
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### Training Hardware |
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- **On Cloud**: No |
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- **GPU Model**: 1 x NVIDIA GeForce RTX 3090 |
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- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K |
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- **RAM Size**: 31.78 GB |
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### Framework Versions |
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- Python: 3.9.16 |
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- SpanMarker: 1.3.1.dev |
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- Transformers : 4.29.2 |
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- PyTorch: 2.0.1+cu118 |
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- Datasets: 2.14.3 |
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- Tokenizers: 0.13.2 |
