
# flan-t5-base for Extractive QA

This is the [flan-t5-base](https://huggingface.co/google/flan-t5-base) 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.

**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-base  
**Language:** English  
**Downstream-task:** Extractive QA  
**Training data:** SQuAD 2.0  
**Eval data:** SQuAD 2.0  
**Infrastructure**: 1x NVIDIA 3070  

## Model Usage
```python
import torch
from transformers import(
  AutoModelForQuestionAnswering,
  AutoTokenizer,
  pipeline
)
model_name = "sjrhuschlee/flan-t5-base-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': 0.980, '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": 79.97638326585695,
    "eval_HasAns_f1": 86.1444296592862,
    "eval_HasAns_total": 5928,
    "eval_NoAns_exact": 84.42388561816652,
    "eval_NoAns_f1": 84.42388561816652,
    "eval_NoAns_total": 5945,
    "eval_best_exact": 82.2033184536343,
    "eval_best_exact_thresh": 0.0,
    "eval_best_f1": 85.28292588395921,
    "eval_best_f1_thresh": 0.0,
    "eval_exact": 82.2033184536343,
    "eval_f1": 85.28292588395928,
    "eval_runtime": 522.0299,
    "eval_samples": 12001,
    "eval_samples_per_second": 22.989,
    "eval_steps_per_second": 0.96,
    "eval_total": 11873
}

# Squad
{
    "eval_exact_match": 86.3197729422895,
    "eval_f1": 92.94686836210295,
    "eval_runtime": 442.1088,
    "eval_samples": 10657,
    "eval_samples_per_second": 24.105,
    "eval_steps_per_second": 1.007
}
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0

### Training results



### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

{"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["question-answering", "squad", "squad_v2", "t5"], "datasets": ["squad_v2", "squad"], "pipeline_tag": "question-answering", "model-index": [{"name": "sjrhuschlee/flan-t5-base-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": 82.203, "name": "Exact Match"}, {"type": "f1", "value": 85.283, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 86.367, "name": "Exact Match"}, {"type": "f1", "value": 92.965, "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": 34.167, "name": "Exact Match"}, {"type": "f1", "value": 46.911, "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": 80.862, "name": "Exact Match"}, {"type": "f1", "value": 86.07, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts amazon", "type": "squadshifts", "config": "amazon", "split": "test"}, "metrics": [{"type": "exact_match", "value": 71.624, "name": "Exact Match"}, {"type": "f1", "value": 85.113, "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": 82.389, "name": "Exact Match"}, {"type": "f1", "value": 91.259, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts nyt", "type": "squadshifts", "config": "nyt", "split": "test"}, "metrics": [{"type": "exact_match", "value": 83.736, "name": "Exact Match"}, {"type": "f1", "value": 91.675, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts reddit", "type": "squadshifts", "config": "reddit", "split": "test"}, "metrics": [{"type": "exact_match", "value": 72.743, "name": "Exact Match"}, {"type": "f1", "value": 84.273, "name": "F1"}]}]}]}
transformers pytorch safetensors t5 question-answering squad squad_v2 custom_code en dataset:squad_v2 dataset:squad license:mit model-index endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #question-answering #squad #squad_v2 #custom_code #en #dataset-squad_v2 #dataset-squad #license-mit #model-index #endpoints_compatible #text-generation-inference #region-us 


# flan-t5-base for Extractive QA

This is the flan-t5-base 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.

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-base  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Infrastructure: 1x NVIDIA 3070  

## Model Usage


## Metrics



## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0

### Training results



### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

# flan-t5-base for Extractive QA

This is the flan-t5-base 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.

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-base  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Infrastructure: 1x NVIDIA 3070 ## Model Usage ## Metrics ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0 ### Training results ### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #safetensors #t5 #question-answering #squad #squad_v2 #custom_code #en #dataset-squad_v2 #dataset-squad #license-mit #model-index #endpoints_compatible #text-generation-inference #region-us 
 # flan-t5-base for Extractive QA

This is the flan-t5-base 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.

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-base  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Infrastructure: 1x NVIDIA 3070 ## Model Usage ## Metrics ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0 ### Training results ### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3