flan t5 base

---
language:

• en

• fr

• ro

• de

• multilingual

tags:

• text2text-generation

widget:

• text: "Translate to German: My name is Arthur"

• text: "Please answer to the following question. Who is going to be the next Ballon d'or?"

• text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."

• text: "Please answer the following question. What is the boiling point of Nitrogen?"

• text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?"

• text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"

• text: "Q: ( False or not False or False ) is? A: Let's think step by step"

• text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"

• 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?"

datasets:

• svakulenk0/qrecc

• taskmaster2

• djaym7/wiki_dialog

• deepmind/code_contests

• lambada

• gsm8k

• aqua_rat

• esnli

• quasc

• qed

license: apache-2.0
---

Model Card for FLAN-T5 base

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan2_architecture.jpg"
alt="drawing" width="600"/>

Table of Contents

0. TL;DR
1. Model Details
2. Usage
3. Uses
4. Bias, Risks, and Limitations
5. Training Details
6. Evaluation
7. Environmental Impact
8. Citation
9. Model Card Authors

TL;DR

If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages.
As mentioned in the first few lines of the abstract :
> Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.

Disclaimer: Content from this model card has been written by the Hugging Face team, and parts of it were copy pasted from the T5 model card.

Model Details

Model Description

• Model type: Language model

• Language(s) (NLP): English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian

• License: Apache 2.0

• Related Models: All FLAN-T5 Checkpoints

• Original Checkpoints: All Original FLAN-T5 Checkpoints

• Resources for more information:

Usage

Find below some example scripts on how to use the model in transformers:

Using the Pytorch model

Running the model on a CPU

<details>
<summary> Click to expand </summary>

from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")
input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

</details>

Running the model on a GPU

<details>
<summary> Click to expand </summary>

# pip install accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", device_map="auto")
input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

</details>

Running the model on a GPU using different precisions

#### FP16

<details>
<summary> Click to expand </summary>

# pip install accelerate
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", device_map="auto", torch_dtype=torch.float16)
input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

</details>

#### INT8

<details>
<summary> Click to expand </summary>

# pip install bitsandbytes accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", device_map="auto", load_in_8bit=True)
input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

</details>

Uses

Direct Use and Downstream Use

The authors write in the original paper's model card that:

> The primary use is research on language models, including: research on zero-shot NLP tasks and in-context few-shot learning NLP tasks, such as reasoning, and question answering; advancing fairness and safety research, and understanding limitations of current large language models

See the research paper for further details.

Out-of-Scope Use

More information needed.

Bias, Risks, and Limitations

The information below in this section are copied from the model's official model card:

> Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.

Ethical considerations and risks

> Flan-T5 is fine-tuned on a large corpus of text data that was not filtered for explicit content or assessed for existing biases. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data.

Known Limitations

> Flan-T5 has not been tested in real world applications.

Sensitive Use:

> Flan-T5 should not be applied for any unacceptable use cases, e.g., generation of abusive speech.

Training