Instructions to use anmol-unitmole/longformer-qasper-document-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anmol-unitmole/longformer-qasper-document-qa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="anmol-unitmole/longformer-qasper-document-qa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("anmol-unitmole/longformer-qasper-document-qa") model = AutoModelForQuestionAnswering.from_pretrained("anmol-unitmole/longformer-qasper-document-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,401 Bytes
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{
"model_name": "BERT truncated 512",
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"evidence_recovered": 0.26,
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"average_latency_seconds": 0.028679482998559252,
"p95_latency_seconds": 0.03750500498281326,
"throughput_examples_per_second": 34.86813203885984,
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{
"model_name": "Longformer SQuAD sliding windows",
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},
{
"model_name": "Longformer QASPER fine-tuned",
"examples": 200,
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"token_f1": 0.26664085976583785,
"evidence_recovered": 0.49,
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"average_latency_seconds": 0.26518189649854323,
"p95_latency_seconds": 0.44097381501051114,
"throughput_examples_per_second": 3.7709964865776326,
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}
] |