model documentation (#1)

- model documentation (848ecad96833deb497146f98065e146313d925e9)


Co-authored-by: Nazneen Rajani <nazneen@users.noreply.huggingface.co>

README.md

@@ -1,26 +1,175 @@
 ---
 language:
 - ru
+
 tags:
 - sentiment
 - text-classification
+
 datasets:
 - Tatyana/ru_sentiment_dataset
 ---
 
-# RuBERT for Sentiment Analysis
-Russian texts sentiment classification.
 
+# Model Card for RuBERT for Sentiment Analysis
+ 
+# Model Details
+ 
+## Model Description
+ 
+Russian texts sentiment classification. 
+ 
+- **Developed by:** Tatyana Voloshina
+- **Shared by [Optional]:**  Tatyana Voloshina
+- **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/T-Sh/Sentiment-Analysis)
+ 	
+
+
+# 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
+ 
 Model trained on [Tatyana/ru_sentiment_dataset](https://huggingface.co/datasets/Tatyana/ru_sentiment_dataset)
+ 
+## Training Procedure
+
+ 
+### Preprocessing
+ 
+More information needed 
+ 
+ 
+### Speeds, Sizes, Times
+More information needed 
 
+ 
+# Evaluation
+ 
+ 
+## Testing Data, Factors & Metrics
+ 
+### Testing Data
+ 
+More information needed 
+ 
+ 
+### Factors
+More information needed
+ 
+### Metrics
+ 
+More information needed
+ 
+ 
+## Results 
+ 
+More information needed
+
+ 
+# Model Examination
+ 
 ## Labels meaning
     0: NEUTRAL
     1: POSITIVE
     2: NEGATIVE
 
-## How to use
-```python
+ 
+# 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
 
+More information needed.
+ 
+ 
+ 
+ 
+# Glossary [optional]
+More information needed 
+ 
+# More Information [optional]
+More information needed 
+
+ 
+# Model Card Authors [optional]
+ 
+Tatyana Voloshina 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>
+
+Needed pytorch trained model presented in [Drive](https://drive.google.com/drive/folders/1EnJBq0dGfpjPxbVjybqaS7PsMaPHLUIl?usp=sharing).
+
+Load and place model.pth.tar in folder next to another files of a model.
+
+```python
+ 
 !pip install tensorflow-gpu
 !pip install deeppavlov
 !python -m deeppavlov install squad_bert
@@ -32,9 +181,6 @@ from deeppavlov import build_model
 
 model = build_model(path_to_model/rubert_sentiment.json)
 model(["Сегодня хорошая погода", "Я счастлив проводить с тобою время", "Мне нравится эта музыкальная композиция"])
+ ```
+</details>
 
-```
-
-Needed pytorch trained model presented in [Drive](https://drive.google.com/drive/folders/1EnJBq0dGfpjPxbVjybqaS7PsMaPHLUIl?usp=sharing).
-
-Load and place model.pth.tar in folder next to another files of a model.