This checkpoint should be loaded into BartForConditionalGeneration.frompretrained. See the BART docs for more information.
Open-weight model · Summarization
bert-small2bert-small-finetuned-cnn_daily_mail-summarization
by Manuel Romero mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization
This model is a warm-started BERT2BERT (small) model fine-tuned on the CNN/Dailymail summarization dataset. The model achieves a 17.37 ROUGE-2 score on CNN/Dailymail's test dataset.
Parameters—
Context—
Weights247.2 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads3.7k
Model Card
By Manuel Romero, published under apache-2.0, revision 3ecce850ed19.
This model is a warm-started BERT2BERT (small) model fine-tuned on the CNN/Dailymail summarization dataset. The model achieves a 17.37 ROUGE-2 score on CNN/Dailymail's test dataset. For more details on how the model was fine-tuned, please refer to this notebook.
Read Manuel Romero's full model card
Bert-small2Bert-small Summarization with EncoderDecoder Framework
This model is a warm-started BERT2BERT (small) model fine-tuned on the CNN/Dailymail summarization dataset.
The model achieves a 17.37 ROUGE-2 score on CNN/Dailymail's test dataset.
For more details on how the model was fine-tuned, please refer to this notebook.
Results on test set
| Metric | # Value |
|---|---|
| ROUGE-2 | 17.37 |
Model in Action
from transformers import BertTokenizerFast, EncoderDecoderModel
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer = BertTokenizerFast.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization')
model = EncoderDecoderModel.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization').to(device)
def generate_summary(text):
# cut off at BERT max length 512
inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
input_ids = inputs.input_ids.to(device)
attention_mask = inputs.attention_mask.to(device)
output = model.generate(input_ids, attention_mask=attention_mask)
return tokenizer.decode(output[0], skip_special_tokens=True)
text = "your text to be summarized here..."
generate_summary(text)
Created by Manuel Romero/@mrm8488 | LinkedIn
Made with in Spain
Configuration
- Architecture
- EncoderDecoderModel
- Vocabulary size
- 30,522
- Model type
- encoder-decoder
Identity and Version
- Repository
- mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization
- Publisher
- Manuel Romero
- Task
- Summarization
- Modality
- Text
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- en
- Revision
- 3ecce850ed191e6b576e0fb306b30d5da087c2eb
- First published
- 2022-03-02
- Last updated
- 2020-12-11
Files and Weights
8 files, 247.4 MB in total. The weights are 2 files totalling 247.2 MB in bin.
Weights2 files · 247.2 MB
Configuration2 files · 3.7 KB
Tokenizer2 files · 231.8 KB
Documentation1 file · 1.8 KB
Repository1 file · 345 B
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| pytorch_model.bin | Weights | 247.2 MB | e755e8e29887 |
| training_args.bin | Weights | 2.0 KB | 44c10ac720f9 |
| config.json | Configuration | 3.6 KB | — |
| special_tokens_map.json | Configuration | 112 B | — |
| README.md | Documentation | 1.8 KB | — |
| .gitattributes | Repository | 345 B | — |
| tokenizer_config.json | Tokenizer | 324 B | — |
| vocab.txt | Tokenizer | 231.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 247.2 MB
Download from Manuel Romero
Released by Manuel Romero through its official repository on Hugging Face. Read the license.
Built From
- Trained on (disclosed) cnn_dailymail
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 247.2 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About bert-small2bert-small-finetuned-cnn_daily_mail-summarization
Can I use bert-small2bert-small-finetuned-cnn_daily_mail-summarization commercially?
Yes. bert-small2bert-small-finetuned-cnn_daily_mail-summarization is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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This checkpoint should be loaded into BartForConditionalGeneration.frompretrained. See the BART docs for more information.
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