model documentation

README.md

@@ -1,16 +1,177 @@
 ---
 language:
 - ru
+
 tags:
 - PyTorch
 - Transformers
+
 thumbnail: "https://github.com/sberbank-ai/model-zoo"
 ---
-# ruT5-base
-Model was trained by [SberDevices](https://sberdevices.ru/) team.  
-* Task: `text2text generation`
+
+
+# Model Card for ruT5-base
+ 
+# Model Details
+ 
+## Model Description
+ 
+More information needed  
+ 
+- **Developed by:** [SberDevices](https://sberdevices.ru/) team
+- **Shared by [Optional]:** [SberDevices](https://sberdevices.ru/) team
+- **Model type:** Text2text Generation 
+- **Language(s) (NLP):** Russian
+- **License:** More information needed 
+- **Parent Model:** T5 base
+- **Resources for more information:** More information neeeded
+ 	
+
+
+# Uses
+ 
+
+## Direct Use
+This model can be used for the task of text2text generation 
+ 
+## 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
+ 
+* Dict size: `32 101`
+* Training Data Volume `300 GB`
+ 
+## Training Procedure
+
+ 
+### Preprocessing
+ 
+More information needed 
+ 
+ 
+
+
+ 
+### Speeds, Sizes, Times
 * Type: `encoder-decoder`
 * Tokenizer: `bpe`
-* Dict size: `32 101`
 * Num Parameters: `222 M`	
-* Training Data Volume `300 GB`
+
+ 
+# Evaluation
+ 
+ 
+## Testing Data, Factors & Metrics
+ 
+### Testing Data
+ 
+More information needed 
+ 
+ 
+### 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
+
+* Type: `encoder-decoder`
+
+ 
+## 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]
+ 
+[SberDevices](https://sberdevices.ru/) team 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 AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("sberbank-ai/ruT5-base")
+
+model = AutoModelForSeq2SeqLM.from_pretrained("sberbank-ai/ruT5-base")
+ ```
+</details>
+