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  ---
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- library_name: transformers
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- tags: []
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 馃 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- 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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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Training Details
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- ### Training Data
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- <!-- 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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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- 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).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### 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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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- 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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- [More Information Needed]
 
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  ---
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+ language: en
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+ tags:
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+ - question-answering
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+ - squad
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+ - gpt2
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+ license: mit
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+ datasets:
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+ - squad
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  ---
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+ # GPT-2 Fine-tuned for Question Answering
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+ This model is a GPT-2 model fine-tuned on the SQuAD (Stanford Question Answering Dataset) for question answering tasks. It takes a context and a question as input and generates a concise, accurate answer based on the provided context.
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+ ## Model Description
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+ - **Model Type:** GPT-2
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+ - **Language:** English
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+ - **Training Data:** SQuAD dataset
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+ - **Input Format:** "Context: [context] Question: [question] Answer:"
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+ - **Output Format:** Direct answer without any additional formatting
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+
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+ ## Usage
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+ ```python
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+ from transformers import GPT2LMHeadModel, GPT2Tokenizer
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+
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+ # Load model and tokenizer
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+ model = GPT2LMHeadModel.from_pretrained("your-username/your-model-name")
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+ tokenizer = GPT2Tokenizer.from_pretrained("your-username/your-model-name")
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+
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+ # Prepare input
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+ context = "George Washington was the first president of the United States, serving from 1789 to 1797."
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+ question = "Who was the first president of the United States?"
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+ input_text = f"Context: {context} Question: {question} Answer:"
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+
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+ # Tokenize
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+ inputs = tokenizer(input_text, return_tensors="pt")
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+
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+ # Generate answer
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+ outputs = model.generate(
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+ **inputs,
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+ max_new_tokens=30,
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+ temperature=0.1,
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+ top_k=50,
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+ )
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+
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+ # Decode and extract answer
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+ generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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+ answer = generated_text.split("Answer:")[-1].strip()
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+ print(f"Answer: {answer}")
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+ ```
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+
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+ ## Example Outputs
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+ 1. **Factual Questions:**
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+ ```
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+ Context: George Washington was the first president of the United States, serving from 1789 to 1797.
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+ Question: Who was the first president of the United States?
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+ Answer: George Washington
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+ ```
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+
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+ 2. **Date Questions:**
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+ ```
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+ Context: The Declaration of Independence was signed on July 4, 1776, by the Continental Congress in Philadelphia.
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+ Question: When was the Declaration of Independence signed?
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+ Answer: July 4 1776
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+ ```
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+ 3. **Location Questions:**
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+ ```
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+ Context: Paris is the capital and largest city of France, located on the river Seine.
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+ Question: What is the capital of France?
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+ Answer: Paris
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+ ```
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+ 4. **Measurement Questions:**
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+ ```
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+ Context: The Eiffel Tower was completed in 1889 and stands at a height of 324 meters.
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+ Question: How tall is the Eiffel Tower?
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+ Answer: 324 meters
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+ ```
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+ ## Model Performance
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+ The model demonstrates strong performance in:
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+ - Extracting precise information from context
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+ - Providing concise answers
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+ - Handling various question types (who, what, when, where, how)
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+ - Maintaining accuracy with numerical values and dates
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+ ## Limitations
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+ - The model requires context to be provided along with the question
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+ - Best suited for factual questions rather than opinion or analysis
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+ - Context length is limited to the model's maximum sequence length
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  ## Training Details
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+ The model was fine-tuned using:
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+ - Base model: GPT-2
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+ - Training dataset: SQuAD
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+ - Training parameters:
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+ - Learning rate: 2e-5
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+ - Batch size: 16
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+ - Mixed precision: bfloat16
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## License
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+ This model is released under the MIT license.