Super-squash branch 'main' using huggingface_hub

Co-authored-by: SFconvertbot <SFconvertbot@users.noreply.huggingface.co>

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README.md

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+---
+
+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
+    
+---
+
+# 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.

config.json

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+{
+  "_name_or_path": "google/t5-v1_1-base",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "early_stopping": true,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "length_penalty": 3.5,
+  "max_length": 512,
+  "min_length": 32,
+  "model_type": "t5",
+  "no_repeat_ngram_size": 3,
+  "num_beams": 5,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.16.2",
+  "use_cache": false,
+  "vocab_size": 32128
+}

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