---
license: mit
datasets:
- stanfordnlp/imdb
- stanfordnlp/sst2
- Iliab/emotion_dataset
- fancyzhx/ag_news
- CogComp/trec
- microsoft/ms_marco
- CoIR-Retrieval/CodeSearchNet-go-queries-corpus
- CoIR-Retrieval/CodeSearchNet-ccr-javascript-queries-corpus
- KomeijiForce/CommonsenseQA-Explained-by-ChatGPT
- Skylion007/openwebtext
- takala/financial_phrasebank
language:
- fa
- en
- es
- ru
- de
metrics:
- accuracy
- precision
- f1
- recall
- roc_auc
- bleu
- rouge
- perplexity
- mse
library_name: transformers
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

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- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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- **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]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses

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### Direct Use

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[More Information Needed]

### 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

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

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## Bias, Risks, and Limitations

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### Recommendations

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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.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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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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- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **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]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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from datasets import load_dataset, load_metric

# بارگذاری مجموعه داده IMDB
dataset = load_dataset('imdb')

# بارگذاری معیارهای ارزیابی
accuracy_metric = load_metric('accuracy')
precision_metric = load_metric('precision')
recall_metric = load_metric('recall')
f1_metric = load_metric('f1')

# نمونه‌ای از نحوه استفاده از معیارهای ارزیابی
predictions = [0, 1, 1, 0]  # پیش‌بینی‌ها
references = [0, 1, 0, 0]  # مقادیر واقعی

accuracy = accuracy_metric.compute(predictions=predictions, references=references)
precision = precision_metric.compute(predictions=predictions, references=references)
recall = recall_metric.compute(predictions=predictions, references=references)
f1 = f1_metric.compute(predictions=predictions, references=references)

print(f"Accuracy: {accuracy['accuracy']}")
print(f"Precision: {precision['precision']}")
print(f"Recall: {recall['recall']}")
print(f"F1 Score: {f1['f1']}")
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

# بارگذاری مدل و tokenizer
model_name = "نام مدل آموزش‌دیده شما"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# ایجاد pipeline برای تحلیل احساسات
sentiment_analysis = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

# تحلیل احساسات یک متن
text = "I love using Hugging Face transformers!"
result = sentiment_analysis(text)
print(result)
from transformers import AutoModelForCausalLM

# بارگذاری مدل و tokenizer
model_name = "نام مدل آموزش‌دیده شما"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# ایجاد pipeline برای تولید متن
text_generation = pipeline("text-generation", model=model, tokenizer=tokenizer)

# تولید متن
prompt = "Once upon a time"
generated_text = text_generation(prompt, max_length=50)
print(generated_text)
from transformers import AutoModelForSeq2SeqLM

# بارگذاری مدل و tokenizer
model_name = "نام مدل آموزش‌دیده شما"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# ایجاد pipeline برای ترجمه
translation = pipeline("translation_en_to_fr", model=model, tokenizer=tokenizer)

# ترجمه یک متن
text = "How are you?"
translated_text = translation(text)
print(translated_text)
from transformers import AutoModelForQuestionAnswering

# بارگذاری مدل و tokenizer
model_name = "نام مدل آموزش‌دیده شما"
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# ایجاد pipeline برای پاسخ به سوالات
question_answering = pipeline("question-answering", model=model, tokenizer=tokenizer)

# پاسخ به یک سوال
context = "Hugging Face is creating a tool that democratizes AI."
question = "What is Hugging Face creating?"
answer = question_answering(question=question, context=context)
print(answer)
from flask import Flask, request, jsonify
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer

app = Flask(__name__)

# بارگذاری مدل و tokenizer
model_name = "نام مدل آموزش‌دیده شما"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# ایجاد pipeline برای تحلیل احساسات
sentiment_analysis = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

@app.route('/analyze', methods=['POST'])
def analyze():
    data = request.json
    text = data['text']
    result = sentiment_analysis(text)
    return jsonify(result)

if __name__ == '__main__':
    app.run(debug=True)