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README.md
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| 1 |
+
Quantization made by Richard Erkhov.
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| 2 |
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| 3 |
+
[Github](https://github.com/RichardErkhov)
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+
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[Discord](https://discord.gg/pvy7H8DZMG)
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+
[Request more models](https://github.com/RichardErkhov/quant_request)
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| 8 |
+
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+
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+
tinyroberta-squad2 - bnb 4bits
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+
- Model creator: https://huggingface.co/deepset/
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+
- Original model: https://huggingface.co/deepset/tinyroberta-squad2/
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| 13 |
+
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| 14 |
+
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| 15 |
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| 16 |
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+
Original model description:
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+
---
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| 19 |
+
language: en
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| 20 |
+
license: cc-by-4.0
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| 21 |
+
datasets:
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| 22 |
+
- squad_v2
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| 23 |
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model-index:
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| 24 |
+
- name: deepset/tinyroberta-squad2
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+
results:
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| 26 |
+
- task:
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| 27 |
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type: question-answering
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name: Question Answering
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| 29 |
+
dataset:
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name: squad_v2
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type: squad_v2
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config: squad_v2
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split: validation
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metrics:
|
| 35 |
+
- type: exact_match
|
| 36 |
+
value: 78.8627
|
| 37 |
+
name: Exact Match
|
| 38 |
+
verified: true
|
| 39 |
+
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDNlZDU4ODAxMzY5NGFiMTMyZmQ1M2ZhZjMyODA1NmFlOGMxNzYxNTA4OGE5YTBkZWViZjBkNGQ2ZmMxZjVlMCIsInZlcnNpb24iOjF9.Wgu599r6TvgMLTrHlLMVAbUtKD_3b70iJ5QSeDQ-bRfUsVk6Sz9OsJCp47riHJVlmSYzcDj_z_3jTcUjCFFXBg
|
| 40 |
+
- type: f1
|
| 41 |
+
value: 82.0355
|
| 42 |
+
name: F1
|
| 43 |
+
verified: true
|
| 44 |
+
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTFkMzEzMWNiZDRhMGZlODhkYzcwZTZiMDFjZDg2YjllZmUzYWM5NTgwNGQ2NGYyMDk2ZGQwN2JmMTE5NTc3YiIsInZlcnNpb24iOjF9.ChgaYpuRHd5WeDFjtiAHUyczxtoOD_M5WR8834jtbf7wXhdGOnZKdZ1KclmhoI5NuAGc1NptX-G0zQ5FTHEcBA
|
| 45 |
+
- task:
|
| 46 |
+
type: question-answering
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| 47 |
+
name: Question Answering
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+
dataset:
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name: squad
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type: squad
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| 51 |
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config: plain_text
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| 52 |
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split: validation
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| 53 |
+
metrics:
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| 54 |
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- type: exact_match
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| 55 |
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value: 83.860
|
| 56 |
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name: Exact Match
|
| 57 |
+
- type: f1
|
| 58 |
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value: 90.752
|
| 59 |
+
name: F1
|
| 60 |
+
- task:
|
| 61 |
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type: question-answering
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| 62 |
+
name: Question Answering
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| 63 |
+
dataset:
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| 64 |
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name: adversarial_qa
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| 65 |
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type: adversarial_qa
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| 66 |
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config: adversarialQA
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| 67 |
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split: validation
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| 68 |
+
metrics:
|
| 69 |
+
- type: exact_match
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| 70 |
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value: 25.967
|
| 71 |
+
name: Exact Match
|
| 72 |
+
- type: f1
|
| 73 |
+
value: 37.006
|
| 74 |
+
name: F1
|
| 75 |
+
- task:
|
| 76 |
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type: question-answering
|
| 77 |
+
name: Question Answering
|
| 78 |
+
dataset:
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| 79 |
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name: squad_adversarial
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| 80 |
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type: squad_adversarial
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| 81 |
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config: AddOneSent
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| 82 |
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split: validation
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| 83 |
+
metrics:
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| 84 |
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- type: exact_match
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| 85 |
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value: 76.329
|
| 86 |
+
name: Exact Match
|
| 87 |
+
- type: f1
|
| 88 |
+
value: 83.292
|
| 89 |
+
name: F1
|
| 90 |
+
- task:
|
| 91 |
+
type: question-answering
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| 92 |
+
name: Question Answering
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| 93 |
+
dataset:
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| 94 |
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name: squadshifts amazon
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| 95 |
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type: squadshifts
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| 96 |
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config: amazon
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| 97 |
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split: test
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| 98 |
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metrics:
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| 99 |
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- type: exact_match
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| 100 |
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value: 63.915
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| 101 |
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name: Exact Match
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| 102 |
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- type: f1
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| 103 |
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value: 78.395
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| 104 |
+
name: F1
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| 105 |
+
- task:
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| 106 |
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type: question-answering
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| 107 |
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name: Question Answering
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| 108 |
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dataset:
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| 109 |
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name: squadshifts new_wiki
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| 110 |
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type: squadshifts
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| 111 |
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config: new_wiki
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split: test
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metrics:
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| 114 |
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- type: exact_match
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| 115 |
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value: 80.297
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| 116 |
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name: Exact Match
|
| 117 |
+
- type: f1
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| 118 |
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value: 89.808
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| 119 |
+
name: F1
|
| 120 |
+
- task:
|
| 121 |
+
type: question-answering
|
| 122 |
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name: Question Answering
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| 123 |
+
dataset:
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| 124 |
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name: squadshifts nyt
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| 125 |
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type: squadshifts
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| 126 |
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config: nyt
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split: test
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| 128 |
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metrics:
|
| 129 |
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- type: exact_match
|
| 130 |
+
value: 80.149
|
| 131 |
+
name: Exact Match
|
| 132 |
+
- type: f1
|
| 133 |
+
value: 88.321
|
| 134 |
+
name: F1
|
| 135 |
+
- task:
|
| 136 |
+
type: question-answering
|
| 137 |
+
name: Question Answering
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| 138 |
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dataset:
|
| 139 |
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name: squadshifts reddit
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| 140 |
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type: squadshifts
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| 141 |
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config: reddit
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| 142 |
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split: test
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| 143 |
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metrics:
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| 144 |
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- type: exact_match
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| 145 |
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value: 66.959
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| 146 |
+
name: Exact Match
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| 147 |
+
- type: f1
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| 148 |
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value: 79.300
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| 149 |
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name: F1
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| 150 |
+
---
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| 151 |
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# tinyroberta-squad2
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| 153 |
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| 154 |
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This is the *distilled* version of the [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) model. This model has a comparable prediction quality and runs at twice the speed of the base model.
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## Overview
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| 157 |
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**Language model:** tinyroberta-squad2
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| 158 |
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**Language:** English
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| 159 |
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**Downstream-task:** Extractive QA
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| 160 |
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**Training data:** SQuAD 2.0
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| 161 |
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**Eval data:** SQuAD 2.0
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| 162 |
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**Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system)
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| 163 |
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**Infrastructure**: 4x Tesla v100
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| 164 |
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| 165 |
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## Hyperparameters
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| 166 |
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| 167 |
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```
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batch_size = 96
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n_epochs = 4
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base_LM_model = "deepset/tinyroberta-squad2-step1"
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max_seq_len = 384
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learning_rate = 3e-5
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lr_schedule = LinearWarmup
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warmup_proportion = 0.2
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doc_stride = 128
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max_query_length = 64
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distillation_loss_weight = 0.75
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temperature = 1.5
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teacher = "deepset/robert-large-squad2"
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```
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## Distillation
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This model was distilled using the TinyBERT approach described in [this paper](https://arxiv.org/pdf/1909.10351.pdf) and implemented in [haystack](https://github.com/deepset-ai/haystack).
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+
Firstly, we have performed intermediate layer distillation with roberta-base as the teacher which resulted in [deepset/tinyroberta-6l-768d](https://huggingface.co/deepset/tinyroberta-6l-768d).
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Secondly, we have performed task-specific distillation with [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) as the teacher for further intermediate layer distillation on an augmented version of SQuADv2 and then with [deepset/roberta-large-squad2](https://huggingface.co/deepset/roberta-large-squad2) as the teacher for prediction layer distillation.
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## Usage
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| 188 |
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### In Haystack
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Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/):
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```python
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reader = FARMReader(model_name_or_path="deepset/tinyroberta-squad2")
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# or
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reader = TransformersReader(model_name_or_path="deepset/tinyroberta-squad2")
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```
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### In Transformers
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```python
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
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model_name = "deepset/tinyroberta-squad2"
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# a) Get predictions
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nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
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QA_input = {
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'question': 'Why is model conversion important?',
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'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
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}
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res = nlp(QA_input)
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# b) Load model & tokenizer
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model = AutoModelForQuestionAnswering.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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```
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| 216 |
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## Performance
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| 218 |
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Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
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```
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| 221 |
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"exact": 78.69114798281817,
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| 222 |
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"f1": 81.9198998536977,
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| 223 |
+
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| 224 |
+
"total": 11873,
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| 225 |
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"HasAns_exact": 76.19770580296895,
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| 226 |
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"HasAns_f1": 82.66446878592329,
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| 227 |
+
"HasAns_total": 5928,
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| 228 |
+
"NoAns_exact": 81.17746005046257,
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| 229 |
+
"NoAns_f1": 81.17746005046257,
|
| 230 |
+
"NoAns_total": 5945
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| 231 |
+
```
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| 232 |
+
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| 233 |
+
## Authors
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| 234 |
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**Branden Chan:** branden.chan@deepset.ai
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| 235 |
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**Timo Möller:** timo.moeller@deepset.ai
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| 236 |
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**Malte Pietsch:** malte.pietsch@deepset.ai
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| 237 |
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**Tanay Soni:** tanay.soni@deepset.ai
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| 238 |
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**Michel Bartels:** michel.bartels@deepset.ai
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| 239 |
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## About us
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| 241 |
+
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| 242 |
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<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
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| 243 |
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<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
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| 244 |
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<img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/>
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</div>
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| 246 |
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<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
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| 247 |
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<img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/>
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</div>
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</div>
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[deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.
|
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|
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Some of our other work:
|
| 255 |
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- [roberta-base-squad2]([https://huggingface.co/deepset/roberta-base-squad2)
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| 256 |
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- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
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- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
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## Get in touch and join the Haystack community
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<p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>.
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We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community/join">Discord community open to everyone!</a></strong></p>
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[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
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By the way: [we're hiring!](http://www.deepset.ai/jobs)
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