

T5-small for QA
---

[Google's T5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) pre-trained on the [C4](https://huggingface.co/datasets/c4) dataset, fine-tuned for Question-Answering on [SQuAD v2](https://huggingface.co/datasets/squad_v2) with the following hyperparameters:

```
  optimizer=adamw_hf
  learning_rate=3e-5
  adam_beta1=0.9
  adam_beta2=0.999
  adam_epsilon=1e-08
  num_train_epochs=2
  per_device_train_batch_size=12
```


Usage
---

The input [context and question] has to be prepared in a specific way as follows:

```python
from transformers import pipeline

def prep_input(_context, _question):
    return " ".join(["question:", _question.strip(), "context:", _context.strip()])

t5qa = pipeline("text2text-generation", "fgaim/t5-small-squad-v2")

context = """
Oxygen is a chemical element with symbol O and atomic number 8. It is a member of the chalcogen group on the periodic table and is a highly reactive nonmetal and oxidizing agent that readily forms compounds (notably oxides) with most elements. By mass, oxygen is the third-most abundant element in the universe, after hydrogen and helium. At standard temperature and pressure, two atoms of the element bind to form dioxygen, a colorless and odorless diatomic gas with the formula O.
"""

t5qa(prep_input(context, "How many atoms combine to form dioxygen?"))
# [{'generated_text': 'two'}]

t5qa(prep_input(context, "What element makes up almost half of the earth's crust by mass?"))
# [{'generated_text': 'oxygen'}]

t5qa(prep_input(context, "What are the most abundent elements of the universe by mass?"))
# [{'generated_text': 'hydrogen and helium'}]
```

{"language": ["en"], "license": "apache-2.0", "tags": ["text2text-generation"], "datasets": ["c4", "squad"], "widget": [{"text": "question: What is the atomic number for oxygen? context: Oxygen is a chemical element with symbol O and atomic number 8."}, {"text": "question: What is the chemical symbol of Oxygen? context: Oxygen is a chemical element with symbol O and atomic number 8."}]}
transformers pytorch t5 text2text-generation en dataset:c4 dataset:squad license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-c4 #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 



T5-small for QA
---

Google's T5-small pre-trained on the C4 dataset, fine-tuned for Question-Answering on SQuAD v2 with the following hyperparameters:




Usage
---

The input [context and question] has to be prepared in a specific way as follows:



TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-c4 #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

passage: TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-c4 #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
