
# flan-t5-large for Extractive QA

This is the [flan-t5-large](https://huggingface.co/google/flan-t5-large) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering.

**UPDATE:** With transformers version 4.31.0 the `use_remote_code=True` is no longer necessary.

This model was trained using LoRA available through the [PEFT library](https://github.com/huggingface/peft).

**NOTE:** The `<cls>` token must be manually added to the beginning of the question for this model to work properly. It uses the `<cls>` token to be able to make "no answer" predictions. The t5 tokenizer does not automatically add this special token which is why it is added manually.

## Overview
**Language model:** flan-t5-large  
**Language:** English  
**Downstream-task:** Extractive QA  
**Training data:** SQuAD 2.0  
**Eval data:** SQuAD 2.0  
**Infrastructure**: 1x NVIDIA 3070  

## Model Usage

### Using Transformers
This uses the merged weights (base model weights + LoRA weights) to allow for simple use in Transformers pipelines. It has the same performance as using the weights separately when using the PEFT library.
```python
import torch
from transformers import(
  AutoModelForQuestionAnswering,
  AutoTokenizer,
  pipeline
)
model_name = "sjrhuschlee/flan-t5-large-squad2"

# a) Using pipelines
nlp = pipeline(
  'question-answering',
  model=model_name,
  tokenizer=model_name,
  # trust_remote_code=True, # Do not use if version transformers>=4.31.0
)
qa_input = {
'question': f'{nlp.tokenizer.cls_token}Where do I live?',  # '<cls>Where do I live?'
'context': 'My name is Sarah and I live in London'
}
res = nlp(qa_input)
# {'score': 0.984, 'start': 30, 'end': 37, 'answer': ' London'}

# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(
  model_name,
  # trust_remote_code=True # Do not use if version transformers>=4.31.0
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

question = f'{tokenizer.cls_token}Where do I live?'  # '<cls>Where do I live?'
context = 'My name is Sarah and I live in London'
encoding = tokenizer(question, context, return_tensors="pt")
output = model(
  encoding["input_ids"],
  attention_mask=encoding["attention_mask"]
)

all_tokens = tokenizer.convert_ids_to_tokens(encoding["input_ids"][0].tolist())
answer_tokens = all_tokens[torch.argmax(output["start_logits"]):torch.argmax(output["end_logits"]) + 1]
answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
# 'London'
```

## Metrics

```bash
# Squad v2
{
    "eval_HasAns_exact": 85.08771929824562,
    "eval_HasAns_f1": 90.598422845031,
    "eval_HasAns_total": 5928,
    "eval_NoAns_exact": 88.47771236333053,
    "eval_NoAns_f1": 88.47771236333053,
    "eval_NoAns_total": 5945,
    "eval_best_exact": 86.78514276088605,
    "eval_best_exact_thresh": 0.0,
    "eval_best_f1": 89.53654936623764,
    "eval_best_f1_thresh": 0.0,
    "eval_exact": 86.78514276088605,
    "eval_f1": 89.53654936623776,
    "eval_runtime": 1908.3189,
    "eval_samples": 12001,
    "eval_samples_per_second": 6.289,
    "eval_steps_per_second": 0.787,
    "eval_total": 11873
}

# Squad
{
    "eval_HasAns_exact": 85.99810785241249,
    "eval_HasAns_f1": 91.296119057944,
    "eval_HasAns_total": 10570,
    "eval_best_exact": 85.99810785241249,
    "eval_best_exact_thresh": 0.0,
    "eval_best_f1": 91.296119057944,
    "eval_best_f1_thresh": 0.0,
    "eval_exact": 85.99810785241249,
    "eval_f1": 91.296119057944,
    "eval_runtime": 1508.9596,
    "eval_samples": 10657,
    "eval_samples_per_second": 7.062,
    "eval_steps_per_second": 0.883,
    "eval_total": 10570
}
```

### Using with Peft
**NOTE**: This requires code in the PR https://github.com/huggingface/peft/pull/473 for the PEFT library.
```python
#!pip install peft

from peft import LoraConfig, PeftModelForQuestionAnswering
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model_name = "sjrhuschlee/flan-t5-large-squad2"
```
{"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["question-answering", "squad", "squad_v2", "t5", "lora", "peft"], "datasets": ["squad_v2", "squad"], "base_model": "google/flan-t5-large", "model-index": [{"name": "sjrhuschlee/flan-t5-large-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 86.819, "name": "Exact Match"}, {"type": "f1", "value": 89.569, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 89.357, "name": "Exact Match"}, {"type": "f1", "value": 95.06, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "adversarial_qa", "type": "adversarial_qa", "config": "adversarialQA", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 48.833, "name": "Exact Match"}, {"type": "f1", "value": 62.555, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_adversarial", "type": "squad_adversarial", "config": "AddOneSent", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 84.835, "name": "Exact Match"}, {"type": "f1", "value": 90.245, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts amazon", "type": "squadshifts", "config": "amazon", "split": "test"}, "metrics": [{"type": "exact_match", "value": 76.722, "name": "Exact Match"}, {"type": "f1", "value": 89.68, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts new_wiki", "type": "squadshifts", "config": "new_wiki", "split": "test"}, "metrics": [{"type": "exact_match", "value": 84.316, "name": "Exact Match"}, {"type": "f1", "value": 92.967, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts nyt", "type": "squadshifts", "config": "nyt", "split": "test"}, "metrics": [{"type": "exact_match", "value": 86.925, "name": "Exact Match"}, {"type": "f1", "value": 94.064, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts reddit", "type": "squadshifts", "config": "reddit", "split": "test"}, "metrics": [{"type": "exact_match", "value": 78.241, "name": "Exact Match"}, {"type": "f1", "value": 89.243, "name": "F1"}]}]}]}
transformers pytorch safetensors t5 question-answering squad squad_v2 lora peft custom_code en dataset:squad_v2 dataset:squad base_model:google/flan-t5-large license:mit model-index endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #question-answering #squad #squad_v2 #lora #peft #custom_code #en #dataset-squad_v2 #dataset-squad #base_model-google/flan-t5-large #license-mit #model-index #endpoints_compatible #text-generation-inference #region-us 


# flan-t5-large for Extractive QA

This is the flan-t5-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering.

UPDATE: With transformers version 4.31.0 the 'use_remote_code=True' is no longer necessary.

This model was trained using LoRA available through the PEFT library.

NOTE: The '<cls>' token must be manually added to the beginning of the question for this model to work properly. It uses the '<cls>' token to be able to make "no answer" predictions. The t5 tokenizer does not automatically add this special token which is why it is added manually.

## Overview
Language model: flan-t5-large  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Infrastructure: 1x NVIDIA 3070  

## Model Usage

### Using Transformers
This uses the merged weights (base model weights + LoRA weights) to allow for simple use in Transformers pipelines. It has the same performance as using the weights separately when using the PEFT library.


## Metrics



### Using with Peft
NOTE: This requires code in the PR URL for the PEFT library.

# flan-t5-large for Extractive QA

This is the flan-t5-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering.

UPDATE: With transformers version 4.31.0 the 'use_remote_code=True' is no longer necessary.

This model was trained using LoRA available through the PEFT library.

NOTE: The '<cls>' token must be manually added to the beginning of the question for this model to work properly. It uses the '<cls>' token to be able to make "no answer" predictions. The t5 tokenizer does not automatically add this special token which is why it is added manually. ## Overview
Language model: flan-t5-large  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Infrastructure: 1x NVIDIA 3070 ## Model Usage ### Using Transformers
This uses the merged weights (base model weights + LoRA weights) to allow for simple use in Transformers pipelines. It has the same performance as using the weights separately when using the PEFT library. ## Metrics ### Using with Peft
NOTE: This requires code in the PR URL for the PEFT library.
TAGS
#transformers #pytorch #safetensors #t5 #question-answering #squad #squad_v2 #lora #peft #custom_code #en #dataset-squad_v2 #dataset-squad #base_model-google/flan-t5-large #license-mit #model-index #endpoints_compatible #text-generation-inference #region-us 
 # flan-t5-large for Extractive QA

This is the flan-t5-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering.

UPDATE: With transformers version 4.31.0 the 'use_remote_code=True' is no longer necessary.

This model was trained using LoRA available through the PEFT library.

NOTE: The '<cls>' token must be manually added to the beginning of the question for this model to work properly. It uses the '<cls>' token to be able to make "no answer" predictions. The t5 tokenizer does not automatically add this special token which is why it is added manually. ## Overview
Language model: flan-t5-large  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Infrastructure: 1x NVIDIA 3070 ## Model Usage ### Using Transformers
This uses the merged weights (base model weights + LoRA weights) to allow for simple use in Transformers pipelines. It has the same performance as using the weights separately when using the PEFT library. ## Metrics ### Using with Peft
NOTE: This requires code in the PR URL for the PEFT library.