
# Try out in the Hosted inference API

In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right.

# Model Loading
The model (based on a GPT2 base architecture) can be loaded in the following way:
```
from transformers import GPT2LMHeadModel, GPT2TokenizerFast

model = GPT2LMHeadModel.from_pretrained("philippelaban/summary_loop10")
tokenizer = GPT2TokenizerFast.from_pretrained("philippelaban/summary_loop10")
```

# Example Use
```
document = "Bouncing Boulders Point to Quakes on Mars. A preponderance of boulder tracks on the red planet may be evidence of recent seismic activity. If a rock falls on Mars, and no one is there to see it, does it leave a trace? Yes, and it's a beautiful herringbone-like pattern, new research reveals. Scientists have now spotted thousands of tracks on the red planet created by tumbling boulders. Delicate chevron-shaped piles of Martian dust and sand frame the tracks, the team showed, and most fade over the course of a few years. Rockfalls have been spotted elsewhere in the solar system, including on the moon and even a comet. But a big open question is the timing of these processes on other worlds — are they ongoing or did they predominantly occur in the past?"

tokenized_document = tokenizer([document], max_length=300, truncation=True, return_tensors="pt")["input_ids"].cuda()
input_shape = tokenized_document.shape
outputs = model.generate(tokenized_document, do_sample=False, max_length=500, num_beams=4, num_return_sequences=4, no_repeat_ngram_size=6, return_dict_in_generate=True, output_scores=True)
candidate_sequences = outputs.sequences[:, input_shape[1]:] # Remove the encoded text, keep only the summary
candidate_scores = outputs.sequences_scores.tolist()

for candidate_tokens, score in zip(candidate_sequences, candidate_scores):
    summary = tokenizer.decode(candidate_tokens)
    print("[Score: %.3f] %s" % (score, summary[:summary.index("END")]))
```

# Example output
```
[Score: -0.084]  Here's what you need to know about rockfalls
[Score: -0.087]  Here's what you need to know about these tracks
[Score: -0.091]  Here's what we know so far about these tracks
[Score: -0.101]  Here's what you need to know about rockfall
```

# Github repo

You can access more information, access to the scoring function, the training script, or an example training log on the Github repo: https://github.com/CannyLab/summary_loop
{"language": ["en"], "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail"]}
transformers pytorch gpt2 text-generation summarization en dataset:cnn_dailymail license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# Try out in the Hosted inference API

In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right.

# Model Loading
The model (based on a GPT2 base architecture) can be loaded in the following way:


# Example Use


# Example output


# Github repo

You can access more information, access to the scoring function, the training script, or an example training log on the Github repo: URL
# Try out in the Hosted inference API

In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right. # Model Loading
The model (based on a GPT2 base architecture) can be loaded in the following way: # Example Use # Example output # Github repo

You can access more information, access to the scoring function, the training script, or an example training log on the Github repo: URL
TAGS
#transformers #pytorch #gpt2 #text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Try out in the Hosted inference API

In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right. # Model Loading
The model (based on a GPT2 base architecture) can be loaded in the following way: # Example Use # Example output # Github repo

You can access more information, access to the scoring function, the training script, or an example training log on the Github repo: URL
passage: TAGS
#transformers #pytorch #gpt2 #text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# Try out in the Hosted inference API

In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right.# Model Loading
The model (based on a GPT2 base architecture) can be loaded in the following way:# Example Use# Example output# Github repo

You can access more information, access to the scoring function, the training script, or an example training log on the Github repo: URL