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 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
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-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
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-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-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]
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-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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 ---
+license: mit
+language:
+- ru
+metrics:
+- f1
+- roc_auc
+- precision
+- recall
+pipeline_tag: text-classification
+tags:
+- sentiment-analysis
+- multi-label-classification
+- sentiment analysis
+- rubert
+- sentiment
+- bert
+- tiny
+- russian
+- multilabel
+- classification
+- prompt-classification
 ---
+Модель [RuBERT-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) была fine-tuned для задачи __prompt classification__, предназначенная для __Russian__ текст.
+Выполняет задачу __multi-label classification__ с слудующимим категориями:
+```yaml
+0: admiration
+1: amusement
+2: anger
+```
+Категории для русского языка:
+```yaml
+write: написать
+draw: рисовать
+neutral: нейтральность
+```
+## Usage
+```python
+from transformers import pipeline
+model = pipeline(model="r1char9/rubert-tiny2-clf")
+model('Сгенерируй картину Томаса Шелби')
+# [{'label': 'draw', 'score': 0.8699279427528381}]
+```
+## Metrics:
+```
+   write   draw  neutral  micro avg  macro avg  weighted avg  \
+precision    1.0    1.0      1.0        1.0        1.0           1.0
+recall       1.0    1.0      1.0        1.0        1.0           1.0
+f1-score     1.0    1.0      1.0        1.0        1.0           1.0
+support    155.0  117.0     19.0      291.0      291.0         291.0
+auc-roc      1.0    1.0      1.0        1.0        1.0           1.0
+```