model documentation

README.md

@@ -1,21 +1,170 @@
----
-language:
-- ru
-tags:
-- PyTorch
-- Transformers
-- bert
-- exbert
-pipeline_tag: fill-mask
-thumbnail: "https://github.com/sberbank-ai/model-zoo"
-license: apache-2.0
----
 
-# ruBert-large
-Model was trained by [SberDevices](https://sberdevices.ru/) team.  
+# Model Card for ruBert-large
+ 
+ 
+# Model Details
+ 
+## Model Description
+ 
+ 
+- **Developed by:** Sberbank-ai 
+- **Shared by [Optional]:** Hugging Face
+- **Model type:** Fill-Mask
+- **Language(s) (NLP):** ru
+- **License:** apache-2.0
+- **Related Models:** exbert
+  - **Parent Model:** bert
+- **Resources for more information:** 
+     - [GitHub Repo](https://github.com/sberbank-ai/model-zoo) 
+     - [Associated Paper](https://arxiv.org/abs/1810.04805) 
+ 
+# Uses
+ 
+## Direct Use
+ 
+Fill-Mask
+ 
+## 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 recomendations.
+ 
+ 
+# Training Details
+ 
+## Training Data
+ 
+More information needed
+ 
+## Training Procedure
+ 
+ 
+ 
+### Preprocessing
+ 
+More information needed
+ 
+### Speeds, Sizes, Times
+ 
 * Task: `mask filling`
 * Type: `encoder`
 * Tokenizer: `bpe`
 * Dict size: `120 138`
 * Num Parameters: `178 M`	
-* Training Data Volume `30 GB`
+* Training Data Volume `30 GB`
+ 
+ 
+# Evaluation
+ 
+ 
+ 
+## Testing Data, Factors & Metrics
+ 
+### Testing Data
+ 
+ 
+ 
+More information needed
+ 
+### Factors
+ 
+More information needed
+ 
+### Metrics
+ 
+More information needed
+ 
+## Results 
+ 
+| Model           | Task                 | Type            | Tokenizer | Dict size | Num Parameters  | Training Data Volume |
+|-----------------|----------------------|-----------------|-----------|-----------|-----------------|----------------------|
+| ruBERT-large    | mask filling         | encoder         | bpe       | 120 138   | 427 M         | 30 GB                |
+ 
+# 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:**
+ 
+More information needed
+ 
+**APA:**
+ 
+More information needed
+ 
+# Glossary [optional]
+ 
+More information needed
+ 
+# More Information [optional]
+ 
+More information needed
+# Model Card Authors [optional]
+ 
+ Sberbank-ai  in collaberation 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, AutoModelForTokenClassification
+ 
+tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-ontonotes5")
+ 
+model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-ontonotes5")
+ 
+```
+</details>