From 9e5c4d39ab4300a22f2baa329e57e302748a1eec Mon Sep 17 00:00:00 2001 From: ktrapeznikov Date: Fri, 6 Nov 2020 06:19:59 -0500 Subject: [PATCH] Create README.md (#8312) * Create README.md * Update model_cards/ktrapeznikov/gpt2-medium-topic-news/README.md Co-authored-by: Julien Chaumond --- .../gpt2-medium-topic-news/README.md | 41 +++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100644 model_cards/ktrapeznikov/gpt2-medium-topic-news/README.md diff --git a/model_cards/ktrapeznikov/gpt2-medium-topic-news/README.md b/model_cards/ktrapeznikov/gpt2-medium-topic-news/README.md new file mode 100644 index 0000000000..e09e767416 --- /dev/null +++ b/model_cards/ktrapeznikov/gpt2-medium-topic-news/README.md @@ -0,0 +1,41 @@ +--- +language: +- en +thumbnail: +widget: + - text: "topic: climate article:" +--- + +# GPT2-medium-topic-news + +## Model description + +GPT2-medium fine tuned on a large news corpus conditioned on a topic + +## Intended uses & limitations + +#### How to use + +To generate a news article text conditioned on a topic, prompt model with: +`topic: climate article:` + +The following tags were used during training: +`arts law international science business politics disaster world conflict football sport sports artanddesign environment music film lifeandstyle business health commentisfree books technology media education politics travel stage uk society us money culture religion science news tv fashion uk australia cities global childrens sustainable global voluntary housing law local healthcare theguardian` + +Zero shot generation works pretty well as long as `topic` is a single word and not too specific. + +```python +device = "cuda:0" +tokenizer = AutoTokenizer.from_pretrained("ktrapeznikov/gpt2-medium-topic-news") +model = AutoModelWithLMHead.from_pretrained("ktrapeznikov/gpt2-medium-topic-news") +model.to(device) +topic = "climate" +prompt = tokenizer(f"topic: {topic} article:", return_tensors="pt") +out = model.generate(prompt["input_ids"].to(device), do_sample=True,max_length=500, early_stopping=True, top_p=.9) +print(tokenizer.decode(list(out.cpu()[0]))) +``` + +## Training data + + +## Training procedure