Create README.md
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README.md
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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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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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| 117 |
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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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| 128 |
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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!
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