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--- |
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language: |
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- en |
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- sp |
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- ja |
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- pe |
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- hi |
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- fr |
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- ch |
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- be |
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- gu |
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- ge |
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- te |
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- it |
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- ar |
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- po |
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- ta |
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- ma |
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- ma |
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- or |
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- pa |
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- po |
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- ur |
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- ga |
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- he |
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- ko |
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- ca |
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- th |
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- du |
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- in |
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- vi |
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- bu |
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- fi |
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- ce |
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- la |
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- tu |
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- ru |
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- cr |
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- sw |
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- yo |
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- ku |
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- bu |
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- ma |
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- cz |
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- fi |
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- so |
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- ta |
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- sw |
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- si |
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- ka |
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- zh |
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- ig |
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- xh |
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- ro |
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- ha |
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- es |
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- sl |
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- li |
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- gr |
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- ne |
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- as |
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- no |
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widget: |
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- text: "Translate to German: My name is Arthur" |
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example_title: "Translation" |
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- text: "Please answer to the following question. Who is going to be the next Ballon d'or?" |
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example_title: "Question Answering" |
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- text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering." |
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example_title: "Logical reasoning" |
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- text: "Please answer the following question. What is the boiling point of Nitrogen?" |
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example_title: "Scientific knowledge" |
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- text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?" |
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example_title: "Yes/no question" |
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- text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?" |
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example_title: "Reasoning task" |
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- text: "Q: ( False or not False or False ) is? A: Let's think step by step" |
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example_title: "Boolean Expressions" |
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- text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?" |
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example_title: "Math reasoning" |
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- text: "Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?" |
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example_title: "Premise and hypothesis" |
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tags: |
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- text2text-generation |
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datasets: |
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- svakulenk0/qrecc |
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- taskmaster2 |
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- djaym7/wiki_dialog |
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- deepmind/code_contests |
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- lambada |
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- gsm8k |
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- aqua_rat |
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- esnli |
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- quasc |
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- qed |
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- financial_phrasebank |
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license: apache-2.0 |
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--- |
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# Model Card for LoRA-FLAN-T5 large |
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This repository contains the LoRA (Low Rank Adapters) of `flan-t5-large` that has been fine-tuned on [`financial_phrasebank`](https://huggingface.co./datasets/financial_phrasebank) dataset. |
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## Usage |
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Use this adapter with `peft` library |
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```python |
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# pip install peft transformers |
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import torch |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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peft_model_id = "ybelkada/flan-t5-large-financial-phrasebank-lora" |
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config = PeftConfig.from_pretrained(peft_model_id) |
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model = AutoModelForSeq2SeqLM.from_pretrained( |
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config.base_model_name_or_path, |
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torch_dtype='auto', |
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device_map='auto' |
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) |
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tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
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# Load the Lora model |
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model = PeftModel.from_pretrained(model, peft_model_id) |
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``` |
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Enjoy! |
