Create README.md

README.md

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
+---
+
+# GoldHamster Model
+
+Model for text classification based on the [GoldHamster corpus](https://doi.org/10.5281/zenodo.7152295). [Source code]() is available.
+
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** English
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+- **Repository:** https://github.com/mariananeves/goldhamster
+- **Paper [optional]:** https://europepmc.org/article/ppr/ppr479254
+
+
+## Uses
+
+Model for detecting our eight-label schema 
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Data Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+
+| invertebrates |  in vivo  |    human  |   organs  | primary cell lines | immortal cell lines |  in silico  |   others  |
+| ------------- | --------- | --------- | --------- | ------------------ | ------------------- | ----------- | --------- |
+|      0.95     |    0.88   |    0.86   |    0.82   |         0.75       |         0.83        |     0.75    |    0.78   |
+
+
+
+
+\others & 0.70 & \bf 0.78 & 0.67 & 0.76 \\
+\midrule
+All (average) & 0.79 & \bf 0.83 & 0.80 & 0.80 \\
+
+
+## Citation [optional]
+
+@misc {PPR:PPR479254,
+	Title = {Automatic classification of experimental models in biomedical literature to support searching for alternative methods to animal experiments},
+	Author = {Neves, Mariana and Klippert, Antonina and Knöspel, Fanny and Rudeck, Juliane and Stolz, Ailine and Ban, Zsofia and Becker, Markus and Diederich, Kai and Grune, Barbara and Kahnau, Pia and Ohnesorge, Nils and Pucher, Johannes and Schönfelder, Gilbert and Bert, Bettina and Butzke, Daniel},
+	DOI = {10.21203/rs.3.rs-1526055/v1},
+	Abstract = {&lt;h4&gt;Background: &lt;/h4&gt; European Union legislature requires replacement of animal experiments with alternative methods, whenever such methods are suitable to reach the intended scientific objective. However, searching for alternative methods in the scientific literature is a time-consuming task that requires careful screening of an enormously large number of experimental biomedical publications. The identification of potentially relevant methods, e.g. organ or cell culture models, or computer simulations, can be supported with text mining tools specifically built for this purpose. Such tools are trained (or fine tuned) on relevant data sets labeled by human experts. &lt;h4&gt;Methods:&lt;/h4&gt; We developed the GoldHamster corpus, composed of 1,600 PubMed (Medline) abstracts, in which we manually identified the used experimental model according to a set of eight labels, namely: “in vivo”, “organs”, “primary cells”, “immortal cell lines”, “invertebrates”, “humans”, “in silico” and “other” (models). We recruited 13 annotators with expertise in the biomedical domain and assigned each article to two individuals. Three additional rounds of annotation aimed at improving the quality of the annotations with disagreements in the first round. Furthermore, we conducted various machine learning experiments based on supervised learning to evaluate the suitability of the corpus for our classification task. &lt;h4&gt;Results:&lt;/h4&gt; We obtained more than 7,000 abstract-level annotations for the above labels. The inter-annotator agreement (kappa coefficient) varied among labels, and ranged from 0.63 (for “others”) to 0.82 (for “invertebrates”), with an overall score of 0.74. The best-performing machine learning experiment used the BioBERT pre-trained model with fine-tuning to our corpus, which gained an overall f-score of 0.82. &lt;h4&gt;Conclusions:&lt;/h4&gt; We obtained a high agreement for most of the labels, and our evaluation demonstrated, that our corpus is suitable for training reliable predictive models for automatic classification of biomedical literature according to the used experimental models. Our “Smart feature-based interactive” search tool (SMAFIRA) will employ this classifier for supporting the retrieval of alternative methods to animal experiments. The corpus and the source code will be made available.},
+	Publisher = {Research Square},
+	Year = {2022},
+	URL = {https://doi.org/10.21203/rs.3.rs-1526055/v1},
+}
+
+## Contact
+
+Contact: https://mariananeves.github.io/
+
+