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
| [ | |
| { | |
| "model_name": "BERT truncated 512", | |
| "controlled_target_tokens": 384, | |
| "examples": 12, | |
| "exact_match": 0.4166666666666667, | |
| "token_f1": 0.5696649029982364, | |
| "evidence_recovered": 0.8333333333333334, | |
| "evidence_token_recall": 0.9037037037037038, | |
| "average_latency_seconds": 0.020591975000570528, | |
| "average_windows": 1.0 | |
| }, | |
| { | |
| "model_name": "BERT truncated 512", | |
| "controlled_target_tokens": 768, | |
| "examples": 12, | |
| "exact_match": 0.08333333333333333, | |
| "token_f1": 0.08950617283950617, | |
| "evidence_recovered": 0.16666666666666666, | |
| "evidence_token_recall": 0.27483828629114954, | |
| "average_latency_seconds": 0.02036956666658322, | |
| "average_windows": 1.0 | |
| }, | |
| { | |
| "model_name": "BERT truncated 512", | |
| "controlled_target_tokens": 1536, | |
| "examples": 12, | |
| "exact_match": 0.08333333333333333, | |
| "token_f1": 0.14773763911694945, | |
| "evidence_recovered": 0.16666666666666666, | |
| "evidence_token_recall": 0.3438101394225293, | |
| "average_latency_seconds": 0.02243222499479695, | |
| "average_windows": 1.0 | |
| }, | |
| { | |
| "model_name": "BERT truncated 512", | |
| "controlled_target_tokens": 3072, | |
| "examples": 12, | |
| "exact_match": 0.08333333333333333, | |
| "token_f1": 0.10275835275835277, | |
| "evidence_recovered": 0.08333333333333333, | |
| "evidence_token_recall": 0.2376230463147804, | |
| "average_latency_seconds": 0.025531658340090264, | |
| "average_windows": 1.0 | |
| }, | |
| { | |
| "model_name": "BERT truncated 512", | |
| "controlled_target_tokens": 4608, | |
| "examples": 12, | |
| "exact_match": 0.08333333333333333, | |
| "token_f1": 0.1048951048951049, | |
| "evidence_recovered": 0.08333333333333333, | |
| "evidence_token_recall": 0.2992065754834572, | |
| "average_latency_seconds": 0.028508583335982014, | |
| "average_windows": 1.0 | |
| }, | |
| { | |
| "model_name": "Longformer SQuAD sliding windows", | |
| "controlled_target_tokens": 384, | |
| "examples": 12, | |
| "exact_match": 0.3333333333333333, | |
| "token_f1": 0.4934343434343434, | |
| "evidence_recovered": 0.75, | |
| "evidence_token_recall": 0.8358024691358025, | |
| "average_latency_seconds": 0.06816069167204357, | |
| "average_windows": 1.0 | |
| }, | |
| { | |
| "model_name": "Longformer SQuAD sliding windows", | |
| "controlled_target_tokens": 768, | |
| "examples": 12, | |
| "exact_match": 0.25, | |
| "token_f1": 0.4136738906088751, | |
| "evidence_recovered": 0.5833333333333334, | |
| "evidence_token_recall": 0.729773994424532, | |
| "average_latency_seconds": 0.07133218333668385, | |
| "average_windows": 1.0 | |
| }, | |
| { | |
| "model_name": "Longformer SQuAD sliding windows", | |
| "controlled_target_tokens": 1536, | |
| "examples": 12, | |
| "exact_match": 0.0, | |
| "token_f1": 0.13654970760233917, | |
| "evidence_recovered": 0.25, | |
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| "average_latency_seconds": 0.08053487500001211, | |
| "average_windows": 1.0 | |
| }, | |
| { | |
| "model_name": "Longformer SQuAD sliding windows", | |
| "controlled_target_tokens": 3072, | |
| "examples": 12, | |
| "exact_match": 0.16666666666666666, | |
| "token_f1": 0.3242424242424242, | |
| "evidence_recovered": 0.4166666666666667, | |
| "evidence_token_recall": 0.5802561217227383, | |
| "average_latency_seconds": 0.15887252500397153, | |
| "average_windows": 2.0 | |
| }, | |
| { | |
| "model_name": "Longformer SQuAD sliding windows", | |
| "controlled_target_tokens": 4608, | |
| "examples": 12, | |
| "exact_match": 0.08333333333333333, | |
| "token_f1": 0.1414141414141414, | |
| "evidence_recovered": 0.25, | |
| "evidence_token_recall": 0.4190843997467528, | |
| "average_latency_seconds": 0.23718954166785503, | |
| "average_windows": 3.0 | |
| }, | |
| { | |
| "model_name": "Longformer QASPER fine-tuned", | |
| "controlled_target_tokens": 384, | |
| "examples": 12, | |
| "exact_match": 0.5, | |
| "token_f1": 0.6670454545454545, | |
| "evidence_recovered": 0.8333333333333334, | |
| "evidence_token_recall": 0.9037037037037038, | |
| "average_latency_seconds": 0.07243051666591782, | |
| "average_windows": 1.0 | |
| }, | |
| { | |
| "model_name": "Longformer QASPER fine-tuned", | |
| "controlled_target_tokens": 768, | |
| "examples": 12, | |
| "exact_match": 0.5, | |
| "token_f1": 0.6670454545454545, | |
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| "average_windows": 1.0 | |
| }, | |
| { | |
| "model_name": "Longformer QASPER fine-tuned", | |
| "controlled_target_tokens": 1536, | |
| "examples": 12, | |
| "exact_match": 0.25, | |
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| "average_latency_seconds": 0.07720827500937351, | |
| "average_windows": 1.0 | |
| }, | |
| { | |
| "model_name": "Longformer QASPER fine-tuned", | |
| "controlled_target_tokens": 3072, | |
| "examples": 12, | |
| "exact_match": 0.25, | |
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| "average_latency_seconds": 0.16716532500868198, | |
| "average_windows": 2.0 | |
| }, | |
| { | |
| "model_name": "Longformer QASPER fine-tuned", | |
| "controlled_target_tokens": 4608, | |
| "examples": 12, | |
| "exact_match": 0.25, | |
| "token_f1": 0.4398148148148148, | |
| "evidence_recovered": 0.6666666666666666, | |
| "evidence_token_recall": 0.7818602693602693, | |
| "average_latency_seconds": 0.2461085750013202, | |
| "average_windows": 3.0 | |
| } | |
| ] |