model documentation

README.md

@@ -1,11 +1,171 @@
-\n## BLEURT
+---
+tags:
+- text-classification
+- bert
+---
 
-Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by 
-Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
+# Model Card for bleurt-tiny-512 
+ 
+# Model Details
+ 
+## Model Description
+ 
+Pytorch version of the original BLEURT models from ACL paper
+ 
+- **Developed by:** Elron Bandel, Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research
+- **Shared by [Optional]:** Elron Bandel
+- **Model type:** Text Classification 
+- **Language(s) (NLP):** More information needed
+- **License:** More information needed 
+- **Parent Model:** BERT
+- **Resources for more information:**
+     - [GitHub Repo](https://github.com/google-research/bleurt/tree/master)
+ 	  - [Associated Paper](https://aclanthology.org/2020.acl-main.704/)
+    - [Blog Post](https://ai.googleblog.com/2020/05/evaluating-natural-language-generation.html)
+ 	
 
-The code for model conversion was originated from [this notebook](https://colab.research.google.com/drive/1KsCUkFW45d5_ROSv2aHtXgeBa2Z98r03?usp=sharing) mentioned [here](https://github.com/huggingface/datasets/issues/224).
 
-## Usage Example
+# Uses
+ 
+
+## Direct Use
+This model can be used for the task of Text Classification 
+ 
+## Downstream Use [Optional]
+ 
+More information needed.
+ 
+## Out-of-Scope Use
+ 
+The model should not be used to intentionally create hostile or alienating environments for people. 
+ 
+# Bias, Risks, and Limitations
+ 
+ 
+Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
+
+
+
+## Recommendations
+ 
+ 
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+# Training Details
+ 
+## Training Data
+The model authors note in the [associated paper](https://aclanthology.org/2020.acl-main.704.pdf): 
+> We use years 2017 to 2019 of the WMT Metrics Shared Task, to-English language pairs. For each year, we used the of- ficial WMT test set, which include several thou- sand pairs of sentences with human ratings from the news domain. The training sets contain 5,360, 9,492, and 147,691 records for each year. 
+ 
+ 
+## Training Procedure
+
+ 
+### Preprocessing
+ 
+More information needed 
+ 
+### Speeds, Sizes, Times
+More information needed 
+
+ 
+# Evaluation
+ 
+ 
+## Testing Data, Factors & Metrics
+ 
+### Testing Data
+ 
+The test sets for years 2018 and 2019 [of the WMT Metrics Shared Task, to-English language pairs.]  are noisier,
+ 
+ 
+ 
+### Factors
+More information needed
+ 
+### Metrics
+ 
+More information needed
+ 
+ 
+## Results 
+ 
+More information needed
+
+ 
+# Model Examination
+ 
+More information needed
+ 
+# Environmental Impact
+ 
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+ 
+- **Hardware Type:** More information needed
+- **Hours used:** More information needed
+- **Cloud Provider:** More information needed
+- **Compute Region:** More information needed
+- **Carbon Emitted:** More information needed
+ 
+# Technical Specifications [optional]
+ 
+## Model Architecture and Objective
+
+More information needed 
+ 
+## Compute Infrastructure
+ 
+More information needed 
+ 
+### Hardware
+ 
+ 
+More information needed
+ 
+### Software
+ 
+More information needed.
+ 
+# Citation
+
+ 
+**BibTeX:**
+ 
+ 
+```bibtex
+@inproceedings{sellam2020bleurt,
+  title = {BLEURT: Learning Robust Metrics for Text Generation},
+  author = {Thibault Sellam and Dipanjan Das and Ankur P Parikh},
+  year = {2020},
+  booktitle = {Proceedings of ACL}
+}
+```
+ 
+ 
+ 
+ 
+# Glossary [optional]
+More information needed 
+ 
+# More Information [optional]
+More information needed 
+
+ 
+# Model Card Authors [optional]
+ 
+ Elron Bandel in collaboration with Ezi Ozoani and the Hugging Face team
+
+
+# Model Card Contact
+ 
+More information needed
+ 
+# How to Get Started with the Model
+ 
+Use the code below to get started with the model.
+ 
+<details>
+<summary> Click to expand </summary>
 
 ```python
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
@@ -22,4 +182,7 @@ with torch.no_grad():
   scores = model(**tokenizer(references, candidates, return_tensors='pt'))[0].squeeze()
 
 print(scores) # tensor([-0.9414, -0.5678])
-```
+ ```
+
+See [this notebook](https://colab.research.google.com/drive/1KsCUkFW45d5_ROSv2aHtXgeBa2Z98r03?usp=sharing) for model conversion code. 
+</details>