Summarization
Transformers
PyTorch
Enawené-Nawé
longt5
text2text-generation
Trained with AutoTrain
Eval Results (legacy)
Instructions to use L-macc/autotrain-Biomedical_sc_summ-1217846142 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use L-macc/autotrain-Biomedical_sc_summ-1217846142 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="L-macc/autotrain-Biomedical_sc_summ-1217846142")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("L-macc/autotrain-Biomedical_sc_summ-1217846142") model = AutoModelForSeq2SeqLM.from_pretrained("L-macc/autotrain-Biomedical_sc_summ-1217846142", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1217846142
- CO2 Emissions (in grams): 16.2112
Validation Metrics
- Loss: 2.159
- Rouge1: 40.236
- Rouge2: 12.161
- RougeL: 23.255
- RougeLsum: 35.138
- Gen Len: 121.504
Usage
You can use cURL to access this model:
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://huggingface.co/proxy/api-inference.huggingface.co/L-macc/autotrain-Biomedical_sc_summ-1217846142
- Downloads last month
- 33
Evaluation results
- ROUGE-1 on Blaise-g/SumPubmedtest set self-reported38.232
- ROUGE-2 on Blaise-g/SumPubmedtest set self-reported12.291
- ROUGE-L on Blaise-g/SumPubmedtest set self-reported21.451
- ROUGE-LSUM on Blaise-g/SumPubmedtest set self-reported34.303
- loss on Blaise-g/SumPubmedtest set self-reported2.327
- gen_len on Blaise-g/SumPubmedtest set self-reported130.393