
# GPT2-medium-topic-news

## Model description

GPT2-medium fine tuned on a largish news corpus conditioned on a topic, source, title

## Intended uses & limitations

#### How to use

To generate a news article text conditioned on a topic, source, title or some subsets, prompt model with: 
```python
f"topic {topic} source"
f"topic {topic} source {source} title"
f"topic {topic} source {source} title {title} body"
```

Try the following tags for `topic: climate, weather, vaccination`.

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-small-set")
model = AutoModelWithLMHead.from_pretrained("ktrapeznikov/gpt2-medium-topic-small-set")
model.to(device)
topic = "climate"
prompt = tokenizer(f"topic {topics} source straitstimes title", 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(out[0].cpu(), skip_special_tokens=True))
```
{"language": ["en"], "widget": [{"text": "topic climate source washington post title "}]}
transformers pytorch jax gpt2 text-generation en autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# GPT2-medium-topic-news

## Model description

GPT2-medium fine tuned on a largish news corpus conditioned on a topic, source, title

## Intended uses & limitations

#### How to use

To generate a news article text conditioned on a topic, source, title or some subsets, prompt model with: 


Try the following tags for 'topic: climate, weather, vaccination'.

Zero shot generation works pretty well as long as 'topic' is a single word and not too specific.


# GPT2-medium-topic-news ## Model description

GPT2-medium fine tuned on a largish news corpus conditioned on a topic, source, title ## Intended uses & limitations #### How to use

To generate a news article text conditioned on a topic, source, title or some subsets, prompt model with: 


Try the following tags for 'topic: climate, weather, vaccination'.

Zero shot generation works pretty well as long as 'topic' is a single word and not too specific.
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # GPT2-medium-topic-news ## Model description

GPT2-medium fine tuned on a largish news corpus conditioned on a topic, source, title ## Intended uses & limitations #### How to use

To generate a news article text conditioned on a topic, source, title or some subsets, prompt model with: 


Try the following tags for 'topic: climate, weather, vaccination'.

Zero shot generation works pretty well as long as 'topic' is a single word and not too specific.
passage: TAGS
#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# GPT2-medium-topic-news## Model description

GPT2-medium fine tuned on a largish news corpus conditioned on a topic, source, title## Intended uses & limitations#### How to use

To generate a news article text conditioned on a topic, source, title or some subsets, prompt model with: 


Try the following tags for 'topic: climate, weather, vaccination'.

Zero shot generation works pretty well as long as 'topic' is a single word and not too specific.