File size: 2,439 Bytes
d675c88 7977371 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
---
language:
- en
thumbnail:
tags:
- pytorch
- google/pegasus-reddit_tifu
- summarization
- samsum
license:
datasets:
- samsum
metrics:
- rouge
---
# Samsum Pegasus (Reddit/TIFU) for conversational summaries
## Model description
Pegasus (Reddit/TIFU) for conversational summaries trained on the samsum dataset!
## Training data
The data is the [samsum](https://huggingface.co/datasets/samsum) dataset for conversional summaries.
The initial weigths were from the [google/pegasus-reddit_tifu](https://huggingface.co/google/pegasus-reddit_tifu). The hypothesis being that it would help the convergence on the samsum dataset to have weights trained on a larger summarization dataset first like the Reddit TIFU using casual language.
## Training procedure
Used the _example/seq2seq/run_summarization.py_ script from the transformers source _4.5.0dev0_.
n_epochs: 3,\
batch_size: 8, \
max_source_length: 256,\
max_target_length: 128
## Eval results
eval_gen_len: 35.9939,\
eval_loss: 1.4284523725509644,\
eval_rouge1: 46.5613,\
eval_rouge2: 23.6137,\
eval_rougeL: 37.2397,\
eval_rougeLsum: 42.7126,\
eval_samples_per_second: 4.302
## Example
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
model_name = "jpcorb20/pegasus-large-reddit_tifu-samsum-256"
tokenizer = PegasusTokenizer.from_pretrained(model_name)
model = PegasusForConditionalGeneration.from_pretrained(model_name)
src_text = """Carter: Hey Alexis, I just wanted to let you know that I had a really nice time with you tonight.\r\nAlexis: Thanks Carter. Yeah, I really enjoyed myself as well.\r\nCarter: If you are up for it, I would really like to see you again soon.\r\nAlexis: Thanks Carter, I'm flattered. But I have a really busy week coming up.\r\nCarter: Yeah, no worries. I totally understand. But if you ever want to go grab dinner again, just let me know.\r\nAlexis: Yeah of course. Thanks again for tonight. Carter: Sure. Have a great night.\r\n"""
token_params = dict(max_length=256, truncation=True, padding='longest', return_tensors="pt")
batch = tokenizer(src_text, **token_params)
translated = model.generate(**batch)
decode_params = dict(num_beams=5, min_length=16, max_length=128, length_penalty=2)
tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True, **decode_params)
print(tgt_text) |