
# Fusion-In-Decoder Base on Natural Questions

This trained model is based on the [Fusion-In-Decoder](https://arxiv.org/abs/2007.01282) model, and trained on the [Natural Questions](https://huggingface.co/datasets/natural_questions) dataset.

# Model Details

Model is based on Fusion-In-Decoder, which in turn is based on the `google/flan-t5-base` checkpoint as the base model. For training, we utilized text retrieval for each query, which provides a collection of relevant passages for it.

We note that the passages were retrieved using a corpus based on [Wikipedia](https://huggingface.co/datasets/wiki_dpr).

# Evaluation

See model performance on Evaluation Results tab on the right side.
{"language": ["en"], "license": "cc-by-sa-3.0", "datasets": ["kilt_tasks"], "metrics": ["exact_match"], "pipeline_tag": "text-generation", "model-index": [{"name": "results", "results": [{"task": {"type": "text-generation", "name": "Question Answering"}, "dataset": {"name": "NQ KILT", "type": "kilt_tasks", "args": "nq"}, "metrics": [{"type": "exact_match", "value": 51.55, "name": "Exact Macth"}]}]}]}
transformers pytorch safetensors t5 text2text-generation text-generation en dataset:kilt_tasks arxiv:2007.01282 license:cc-by-sa-3.0 model-index autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #text-generation #en #dataset-kilt_tasks #arxiv-2007.01282 #license-cc-by-sa-3.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


# Fusion-In-Decoder Base on Natural Questions

This trained model is based on the Fusion-In-Decoder model, and trained on the Natural Questions dataset.

# Model Details

Model is based on Fusion-In-Decoder, which in turn is based on the 'google/flan-t5-base' checkpoint as the base model. For training, we utilized text retrieval for each query, which provides a collection of relevant passages for it.

We note that the passages were retrieved using a corpus based on Wikipedia.

# Evaluation

See model performance on Evaluation Results tab on the right side.
# Fusion-In-Decoder Base on Natural Questions

This trained model is based on the Fusion-In-Decoder model, and trained on the Natural Questions dataset. # Model Details

Model is based on Fusion-In-Decoder, which in turn is based on the 'google/flan-t5-base' checkpoint as the base model. For training, we utilized text retrieval for each query, which provides a collection of relevant passages for it.

We note that the passages were retrieved using a corpus based on Wikipedia. # Evaluation

See model performance on Evaluation Results tab on the right side.
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #text-generation #en #dataset-kilt_tasks #arxiv-2007.01282 #license-cc-by-sa-3.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # Fusion-In-Decoder Base on Natural Questions

This trained model is based on the Fusion-In-Decoder model, and trained on the Natural Questions dataset. # Model Details

Model is based on Fusion-In-Decoder, which in turn is based on the 'google/flan-t5-base' checkpoint as the base model. For training, we utilized text retrieval for each query, which provides a collection of relevant passages for it.

We note that the passages were retrieved using a corpus based on Wikipedia. # Evaluation

See model performance on Evaluation Results tab on the right side.
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #text-generation #en #dataset-kilt_tasks #arxiv-2007.01282 #license-cc-by-sa-3.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# Fusion-In-Decoder Base on Natural Questions

This trained model is based on the Fusion-In-Decoder model, and trained on the Natural Questions dataset.# Model Details

Model is based on Fusion-In-Decoder, which in turn is based on the 'google/flan-t5-base' checkpoint as the base model. For training, we utilized text retrieval for each query, which provides a collection of relevant passages for it.

We note that the passages were retrieved using a corpus based on Wikipedia.# Evaluation

See model performance on Evaluation Results tab on the right side.