Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
- 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]
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
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.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- 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 [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
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)