Text2Text Generation
60 languages
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ybelkada HF staff
Update README.md
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---
language:
- en
- sp
- ja
- pe
- hi
- fr
- ch
- be
- gu
- ge
- te
- it
- ar
- po
- ta
- ma
- ma
- or
- pa
- po
- ur
- ga
- he
- ko
- ca
- th
- du
- in
- vi
- bu
- fi
- ce
- la
- tu
- ru
- cr
- sw
- yo
- ku
- bu
- ma
- cz
- fi
- so
- ta
- sw
- si
- ka
- zh
- ig
- xh
- ro
- ha
- es
- sl
- li
- gr
- ne
- as
- no
widget:
- text: "Translate to German: My name is Arthur"
example_title: "Translation"
- text: "Please answer to the following question. Who is going to be the next Ballon d'or?"
example_title: "Question Answering"
- text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."
example_title: "Logical reasoning"
- text: "Please answer the following question. What is the boiling point of Nitrogen?"
example_title: "Scientific knowledge"
- text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?"
example_title: "Yes/no question"
- text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"
example_title: "Reasoning task"
- text: "Q: ( False or not False or False ) is? A: Let's think step by step"
example_title: "Boolean Expressions"
- text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"
example_title: "Math reasoning"
- 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?"
example_title: "Premise and hypothesis"
tags:
- text2text-generation
datasets:
- svakulenk0/qrecc
- taskmaster2
- djaym7/wiki_dialog
- deepmind/code_contests
- lambada
- gsm8k
- aqua_rat
- esnli
- quasc
- qed
- financial_phrasebank
license: apache-2.0
---
# Model Card for LoRA-FLAN-T5 large
![model image](https://s3.amazonaws.com/moonup/production/uploads/1666363435475-62441d1d9fdefb55a0b7d12c.png)
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.
## Usage
Use this adapter with `peft` library
```python
# pip install peft transformers
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
peft_model_id = "ybelkada/flan-t5-large-financial-phrasebank-lora"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(
config.base_model_name_or_path,
torch_dtype='auto',
device_map='auto'
)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)
```
Enjoy!