model documentation

README.md

@@ -1,21 +1,193 @@
 ---
+license: apache-2.0
 language: 
   - en
   - ru
   - multilingual
-license: apache-2.0
 ---
-# XLM-RoBERTa large model whole word masking finetuned on SQuAD
+
+
+# Model Card for xlm-roberta-large-qa-multilingual-finedtuned-ru 
+ 
+# Model Details
+ 
+## Model Description
+ 
+More information needed  
+ 
+- **Developed by:** Alexander Kaigorodov
+- **Shared by [Optional]:** Alexander Kaigorodov
+- **Model type:** Question Answering 
+- **Language(s) (NLP):** English, Russian, Multilingual
+- **License:** Apache 2.0 
+- **Parent Model:** XLM-RoBERTa
+- **Resources for more information:**
+   - [Associated Paper](https://arxiv.org/pdf/1912.09723.pdf) 	
+
+
+# Uses
+ 
+
+## Direct Use
+This model can be used for the task of question answering.
+ 
+## 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
+### XLM-RoBERTa large model whole word masking finetuned on SQuAD
 Pretrained model using a masked language modeling (MLM) objective. 
 Fine tuned on English and Russian QA datasets
-
-## Used QA Datasets
+ 
+### Used QA Datasets
 SQuAD + SberQuAD
+ 
+ 
+## Training Procedure
 
-[SberQuAD original paper](https://arxiv.org/pdf/1912.09723.pdf) is here! Recommend to read!
+ 
+### Preprocessing
+ 
+More information needed 
+ 
+### Speeds, Sizes, Times
+More information needed 
 
-## Evaluation results
+ 
+# Evaluation
+ 
+ 
+## Testing Data, Factors & Metrics
+ 
+### Testing Data
+ 
+More information needed 
+ 
+ 
+### Factors
+More information needed
+ 
+### Metrics
+ 
+More information needed
+ 
+ 
+## Results 
 The results obtained are the following (SberQUaD):
 ```
 f1 = 84.3
 exact_match = 65.3
+```
+
+ 
+# 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
+@incollection{Efimov_2020,
+	doi = {10.1007/978-3-030-58219-7_1},
+  
+	url = {https://doi.org/10.1007%2F978-3-030-58219-7_1},
+  
+	year = 2020,
+	publisher = {Springer International Publishing},
+  
+	pages = {3--15},
+  
+	author = {Pavel Efimov and Andrey Chertok and Leonid Boytsov and Pavel Braslavski},
+  
+	title = {{SberQuAD} {\textendash} Russian Reading Comprehension Dataset: Description and Analysis},
+  
+	booktitle = {Lecture Notes in Computer Science}
+}
+```
+ 
+ 
+ 
+ 
+# Glossary [optional]
+More information needed 
+ 
+# More Information [optional]
+More information needed 
+
+ 
+# Model Card Authors [optional]
+ 
+Alexander Kaigorodov 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, AutoModelForQuestionAnswering
+
+tokenizer = AutoTokenizer.from_pretrained("AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru")
+
+model = AutoModelForQuestionAnswering.from_pretrained("AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru")
+ ```
+</details>