YAML Metadata
Warning:
The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Model description
This model is a fine-tuned version of facebook/bart-large on a dataset in the hub called sunhaozhepy/ag_news_keywords_embeddings to extract main keywords from text. It achieves the following results on the evaluation set:
- Loss: 0.6179
Intended use
from transformers import pipeline
pipe = pipeline('summarization', model='bart_keywords_model')
print(pipe("Aria Opera GPT version - All the browsers come with their own version of AI. So I gave it a try and ask it with LLM it was using. First if all it didn't understand the question. Then I explained and asked which version. I got the usual answer about a language model that is not aware of it's own model I find that curious, but also not transparent. My laptop, software all state their versions and critical information. But something that can easily fool a lot of people doesn't. What I also wonder if the general public will be stuck to ChatGPT 3.5 for ever while better models are behind expensive paywalls."))
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7701 | 0.57 | 500 | 0.7390 |
| 0.5804 | 1.14 | 1000 | 0.7056 |
| 0.5395 | 1.71 | 1500 | 0.6811 |
| 0.4036 | 2.28 | 2000 | 0.6504 |
| 0.3763 | 2.85 | 2500 | 0.6179 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
- Downloads last month
- 9
Model tree for ilsilfverskiold/bart-keyword-extractor
Base model
facebook/bart-large