Initial commit

README.md

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+---
+language: en
+license: cc-by-4.0
+pipeline_tag: summarization
+widget:
+- text: "Example question body."
+---
+
+# Titlewave: t5-base
+
+## Model description
+
+Titlewave is a Chrome extension that helps you choose better titles for your Stack Overflow questions. See https://github.com/tennessejoyce/TitleWave for more information.
+This is one of two NLP models used in the Titlewave project, and its purpose is to suggests a new title based on on the body of the question. The companion model (https://huggingface.co/tennessejoyce/titlewave-bert-base-uncased) classifies whether question will be answered or not just based on the title
+
+## Intended use
+
+Try out different titles for your Stack Overflow post, and see which one gives you the best chance of recieving an answer.
+This model can be used in your browser as a Chrome extension by following the installation instructions at https://github.com/tennessejoyce/TitleWave.
+Or load it in Python like this (which will automatically download the model to your machine):
+
+```python
+>>> from transformers import pipeline
+>>> classifier = pipeline('summarization', model='tennessejoyce/titlewave-t5-base')
+>>> body = """"Example question body."""
+>>> classifier(body)
+
+[{'summary_text': 'Example title suggestion?'}]
+```
+
+
+## Training data
+
+The weights were initialized from the BERT base model (https://huggingface.co/bert-base-uncased), which was trained on BookCorpus and English Wikipedia.
+Then the model was fine-tuned on the dataset of previous Stack Overflow post titles (https://archive.org/details/stackexchange).
+Specifically I used three years of posts from 2017-2019, filtered out posts which were closed, and selected 25% of the remaining posts at random to use in
+the training set. In order to improve the quality of the titles generated, the model was trained only on questions with an accepted answer.
+
+## Evaluation
+
+See https://github.com/tennessejoyce/TitleWave/blob/master/model_training/test_summarizer.ipynb for the performance of the title generation model on the test set.
+

config.json

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+{
+  "_name_or_path": "t5-base",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 40,
+      "min_length": 5,
+      "no_repeat_ngram_size": 1,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    }
+  },
+  "transformers_version": "4.3.2",
+  "use_cache": true,
+  "vocab_size": 32128
+}

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special_tokens_map.json

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tokenizer_config.json

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