File size: 3,208 Bytes
5c0c283 1b01058 d4c2e94 5c0c283 1b01058 5c0c283 1b01058 d4c2e94 1b01058 5c0c283 d4c2e94 5c0c283 08d6e89 5c0c283 d4c2e94 626d682 d4c2e94 2381ce9 d4c2e94 2381ce9 5c0c283 d4c2e94 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 |
---
datasets:
- squad_v2
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
tags:
- deberta
- deberta-v3
- mdeberta
- question-answering
- qa
- multilingual
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
base_model:
- microsoft/mdeberta-v3-base
---
## This model can be used for Extractive QA
It has been finetuned for 3 epochs on [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/).
## Usage
```python
from transformers import pipeline
qa_model = pipeline("question-answering", "timpal0l/mdeberta-v3-base-squad2")
question = "Where do I live?"
context = "My name is Tim and I live in Sweden."
qa_model(question = question, context = context)
# {'score': 0.975547730922699, 'start': 28, 'end': 36, 'answer': ' Sweden.'}
```
## Evaluation on SQuAD2.0 dev set
```bash
{
"epoch": 3.0,
"eval_HasAns_exact": 79.65587044534414,
"eval_HasAns_f1": 85.91387795001529,
"eval_HasAns_total": 5928,
"eval_NoAns_exact": 82.10260723296888,
"eval_NoAns_f1": 82.10260723296888,
"eval_NoAns_total": 5945,
"eval_best_exact": 80.8809904826076,
"eval_best_exact_thresh": 0.0,
"eval_best_f1": 84.00551406448994,
"eval_best_f1_thresh": 0.0,
"eval_exact": 80.8809904826076,
"eval_f1": 84.00551406449004,
"eval_samples": 12508,
"eval_total": 11873,
"train_loss": 0.7729689576483615,
"train_runtime": 9118.953,
"train_samples": 134891,
"train_samples_per_second": 44.377,
"train_steps_per_second": 0.925
}
```
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data.
In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our [paper](https://arxiv.org/abs/2111.09543).
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more implementation details and updates.
mDeBERTa is multilingual version of DeBERTa which use the same structure as DeBERTa and was trained with CC100 multilingual data.
The mDeBERTa V3 base model comes with 12 layers and a hidden size of 768. It has 86M backbone parameters with a vocabulary containing 250K tokens which introduces 190M parameters in the Embedding layer. This model was trained using the 2.5T CC100 data as XLM-R. |