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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 🤗 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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- #### 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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@@ -175,27 +66,3 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
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  **BibTeX:**
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  [More Information Needed]
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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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  ---
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  library_name: transformers
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+ tags:
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+ - factual
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+ - consistency
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+ - hallucination
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+ license: apache-2.0
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+ datasets:
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+ - nkwbtb/SummaCoz
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+ language:
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+ - en
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+ base_model:
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+ - nkwbtb/flan-t5-xxl-bf16
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+ pipeline_tag: text2text-generation
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  ---
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+ # SummaCoz LoRA Adapter for flan-t5-xxl
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  <!-- Provide a quick summary of what the model is/does. -->
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+ The model provides summarization factual consistency classificaiton and explanations.
 
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  ## Model Details
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+ The model for the paper [SummaCoz: A Dataset for Improving the Interpretability of Factual Consistency Detection for Summarization](https://openreview.net/forum?id=1zJrDjKTWg)
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+ Dataset: [`nkwbtb/SummaCoz`](https://huggingface.co/datasets/nkwbtb/SummaCoz)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## Model Usage
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+ ```python
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+ from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
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+ tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xxl")
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+ model = AutoModelForSeq2SeqLM.from_pretrained("nkwbtb/flan-t5-11b-SummaCoz",
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+ torch_dtype="auto",
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+ device_map="auto")
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+ pipe = pipeline("text2text-generation",
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+ model=model,
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+ tokenizer=tokenizer)
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+ PROMPT = """Is the hypothesis true based on the premise? Give your explanation afterwards.
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+ Premise:
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+ {article}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ Hypothesis:
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+ {summary}
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+ """
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+ article = "Goldfish are being caught weighing up to 2kg and koi carp up to 8kg and one metre in length."
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+ summary = "Goldfish are being caught weighing up to 8kg and one metre in length."
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+ print(pipe(PROMPT.format(article=article, summary=summary),
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+ do_sample=False,
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+ max_new_tokens=512))
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+ """[{'generated_text': '\
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+ No, the hypothesis is not true. \
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+ - The hypothesis states that goldfish are being caught weighing up to 8kg and one metre in length. \
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+ - However, the premise states that goldfish are being caught weighing up to 2kg and koi carp up to 8kg and one metre in length. \
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+ - The difference between the two is that the koi carp is weighing 8kg and the goldfish is weighing 2kg.'}]"""
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+ ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Citation [optional]
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  **BibTeX:**
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  [More Information Needed]