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

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README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- t5
+- qa
+- askscience
+- lfqa
+- information retrieval
+datasets:
+- vblagoje/lfqa
+metrics:
+- rouge
+widget:
+- text: why hasn't humanity expanded to live on other 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 is the process that computers use to understand human language in deep
+    learning models?
+  example_title: NLP
+inference:
+  parameters:
+    max_length: 64
+    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
+base_model: google/t5-v1_1-base
+---
+
+
+# checkpoints
+
+- This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the `vblagoje/lfqa` dataset, with training duration of 2 epochs, for a (_somewhat_) apples-to-apples comparison with [t5-base](https://huggingface.co/pszemraj/t5-base-askscience) on the standard eli5 dataset.
+  - This checkpoint does seem to be more coherent than t5-base on the original dataset.
+- Compared to [bart on lfqa](https://huggingface.co/vblagoje/bart_lfqa), it seems to be able to respond to some questions independently of retrieval.
+
+> NOTE: the inference API is limited to generating approx. 64 chars for runtime reasons, for longer outputs try using it in python as a transformers pipeline object.
+
+## Intended uses & limitations
+
+- Q&A, information retrieval
+- it is probably better to use it with a [retrieval pipeline](https://github.com/deepset-ai/haystack) than alone
+
+## Training and evaluation data
+
+- see linked dataset. the dataset was filtered to only included the `askscience` subreddit in an attempt to focus on academic/technical queries.
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 4e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 2
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.16.2
+- Pytorch 1.10.0+cu113
+- Datasets 1.18.3
+- Tokenizers 0.11.0

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": 16,
+  "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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