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Model Details
Don't use the Inference API ( the input box to the right of this page), as that is not optimized for this generation. ---->
Please use the code provided below.
Uses
1 import transformers
2 from transformers import AutoModelForCausalLM, AutoTokenizer
3 model_name = "rony/gpt2-quantized-jokes"
4 tokenizer = AutoTokenizer.from_pretrained(model_name)
5 prompt = tokenizer.apply_chat_template('JOKE: ', add_generation_prompt=True, tokenize=False)
6
7 # Create pipeline
8 pipeline = transformers.pipeline(
9 "text-generation",
10 model=model_name,
11 tokenizer=tokenizer
12 )
13
14 # Generate text
15 sequences = pipeline(
16 prompt,
17 do_sample=True,
18 temperature=0.7,
19 top_p=0.9,
20 num_return_sequences=1,
21 max_length=200,
22 )
23 print(sequences[0]['generated_text'])
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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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.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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- Hours used: [More Information Needed]
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Model Architecture and Objective
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