
# t5 - base- askscience

- [t5-v1_1](https://huggingface.co/google/t5-v1_1-base) trained on the entirety of the _askscience_ sub-section of the eli5 dataset for one epoch.
- compare to bart on eli5 [here](https://huggingface.co/yjernite/bart_eli5)
- note that for the inference API, the model is restricted to outputting 96 tokens - by using the model in python with the transformers library, you can get longer outputs.

## training 

- for inputs, the model was presented with the post title and the post selftext encoded as: `question: <post title> context: <post selftext>`. You may see better results if queries are posed in this fashion.
- The top two replies were aggregated and presented to the model as the output text.
- Training for longer will be explored, but given that the dataset has 127k examples and the loss flatlines at 0.5 epochs so this model should be fairly viable.
{"language": ["en"], "tags": ["t5", "qa", "askscience", "lfqa", "information retrieval"], "datasets": ["eli5"], "metrics": ["rouge"], "widget": [{"text": "why aren't there more planets in our solar system?", "example_title": "solar system"}, {"text": "question: what is a probability distribution? context: I am just learning about statistics.", "example_title": "probability distribution"}, {"text": "question: What are the underlying physical processes by which exercise helps us lose weight? context: I started working out two weeks ago and already feel a lot better, and started to think about it and became deeply confused.", "example_title": "pumpen"}, {"text": "what is a neural network?", "example_title": "deep learning"}, {"text": "What are the primary mechanisms that computers use to understand human language?", "example_title": "NLP"}], "inference": {"parameters": {"max_length": 96, "no_repeat_ngram_size": 2, "encoder_no_repeat_ngram_size": 4, "repetition_penalty": 3.51, "length_penalty": 0.8, "num_beams": 4, "early_stopping": true}}}
transformers pytorch safetensors t5 text2text-generation qa askscience lfqa information retrieval en dataset:eli5 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #qa #askscience #lfqa #information retrieval #en #dataset-eli5 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# t5 - base- askscience

- t5-v1_1 trained on the entirety of the _askscience_ sub-section of the eli5 dataset for one epoch.
- compare to bart on eli5 here
- note that for the inference API, the model is restricted to outputting 96 tokens - by using the model in python with the transformers library, you can get longer outputs.

## training 

- for inputs, the model was presented with the post title and the post selftext encoded as: 'question: <post title> context: <post selftext>'. You may see better results if queries are posed in this fashion.
- The top two replies were aggregated and presented to the model as the output text.
- Training for longer will be explored, but given that the dataset has 127k examples and the loss flatlines at 0.5 epochs so this model should be fairly viable.
# t5 - base- askscience

- t5-v1_1 trained on the entirety of the _askscience_ sub-section of the eli5 dataset for one epoch.
- compare to bart on eli5 here
- note that for the inference API, the model is restricted to outputting 96 tokens - by using the model in python with the transformers library, you can get longer outputs. ## training 

- for inputs, the model was presented with the post title and the post selftext encoded as: 'question: <post title> context: <post selftext>'. You may see better results if queries are posed in this fashion.
- The top two replies were aggregated and presented to the model as the output text.
- Training for longer will be explored, but given that the dataset has 127k examples and the loss flatlines at 0.5 epochs so this model should be fairly viable.
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #qa #askscience #lfqa #information retrieval #en #dataset-eli5 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # t5 - base- askscience

- t5-v1_1 trained on the entirety of the _askscience_ sub-section of the eli5 dataset for one epoch.
- compare to bart on eli5 here
- note that for the inference API, the model is restricted to outputting 96 tokens - by using the model in python with the transformers library, you can get longer outputs. ## training 

- for inputs, the model was presented with the post title and the post selftext encoded as: 'question: <post title> context: <post selftext>'. You may see better results if queries are posed in this fashion.
- The top two replies were aggregated and presented to the model as the output text.
- Training for longer will be explored, but given that the dataset has 127k examples and the loss flatlines at 0.5 epochs so this model should be fairly viable.
passage: TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #qa #askscience #lfqa #information retrieval #en #dataset-eli5 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# t5 - base- askscience

- t5-v1_1 trained on the entirety of the _askscience_ sub-section of the eli5 dataset for one epoch.
- compare to bart on eli5 here
- note that for the inference API, the model is restricted to outputting 96 tokens - by using the model in python with the transformers library, you can get longer outputs.## training 

- for inputs, the model was presented with the post title and the post selftext encoded as: 'question: <post title> context: <post selftext>'. You may see better results if queries are posed in this fashion.
- The top two replies were aggregated and presented to the model as the output text.
- Training for longer will be explored, but given that the dataset has 127k examples and the loss flatlines at 0.5 epochs so this model should be fairly viable.