File size: 9,403 Bytes
09fb982
9030417
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
09fb982
9030417
cc749b9
 
 
 
 
 
 
9030417
cc749b9
9030417
afb24a5
9030417
19d9c40
9030417
b473cf1
9030417
d6d53db
9030417
a213901
9030417
a8bb7b3
9030417
15f7878
9030417
0cd9407
9030417
d400e69
9030417
7215b1a
9030417
ed1cbff
9030417
2099af5
09fb982
 
9030417
09fb982
9030417
09fb982
 
 
9030417
09fb982
9030417
09fb982
9030417
09fb982
 
 
9030417
 
 
 
 
 
09fb982
9030417
09fb982
9030417
09fb982
9030417
 
 
09fb982
9030417
09fb982
9030417
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
09fb982
 
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
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
---
language: en
license: mit
model_details: "\n        ## Abstract\n        This model, 'roberta-finetuned', is\
  \ a question-answering chatbot trained on the SQuAD dataset, demonstrating competency\
  \ in building conversational AI using recent advances in natural language processing.\
  \ It utilizes a BERT model fine-tuned for extractive question answering.\n\n   \
  \     ## Data Collection and Preprocessing\n        The model was trained on the\
  \ Stanford Question Answering Dataset (SQuAD), which contains over 100,000 question-answer\
  \ pairs based on Wikipedia articles. The data preprocessing involved tokenizing\
  \ context paragraphs and questions, truncating sequences to fit BERT's max length,\
  \ and adding special tokens to mark question and paragraph segments.\n\n       \
  \ ## Model Architecture and Training\n        The architecture is based on the BERT\
  \ transformer model, which was pretrained on large unlabeled text corpora. For this\
  \ project, the BERT base model was fine-tuned on SQuAD for extractive question answering,\
  \ with additional output layers for predicting the start and end indices of the\
  \ answer span.\n\n        ## SQuAD 2.0 Dataset\n        SQuAD 2.0 combines the existing\
  \ SQuAD data with over 50,000 unanswerable questions written adversarially by crowdworkers\
  \ to look similar to answerable ones. This version of the dataset challenges models\
  \ to not only produce answers when possible but also determine when no answer is\
  \ supported by the paragraph and abstain from answering.\n        "
intended_use: "\n        - Answering questions from the squad_v2 dataset.\n      \
  \  - Developing question-answering systems within the scope of the aai520-project.\n\
  \        - Research and experimentation in the NLP question-answering domain.\n\
  \        "
limitations_and_bias: "\n        The model inherits limitations and biases from the\
  \ 'roberta-base' model, as it was trained on the same foundational data. \n    \
  \    It may underperform on questions that are ambiguous or too far outside the\
  \ scope of the topics covered in the squad_v2 dataset. \n        Additionally, the\
  \ model may reflect societal biases present in its training data.\n        "
ethical_considerations: "\n        This model should not be used for making critical\
  \ decisions without human oversight, \n        as it can generate incorrect or biased\
  \ answers, especially for topics not covered in the training data. \n        Users\
  \ should also consider the ethical implications of using AI in decision-making processes\
  \ and the potential for perpetuating biases.\n        "
evaluation: "\n        The model was evaluated on the squad_v2 dataset using various\
  \ metrics. These metrics, along with their corresponding scores, \n        are detailed\
  \ in the 'eval_results' section. The evaluation process ensured a comprehensive\
  \ assessment of the model's performance \n        in question-answering scenarios.\n\
  \        "
training: "\n        The model was trained over 4 epochs with a learning rate of 2e-05,\
  \ using a batch size of 128. \n        The training utilized a cross-entropy loss\
  \ function and the AdamW optimizer, with gradient accumulation over 4 steps.\n \
  \       "
tips_and_tricks: "\n        For optimal performance, questions should be clear, concise,\
  \ and grammatically correct. \n        The model performs best on questions related\
  \ to topics covered in the squad_v2 dataset. \n        It is advisable to pre-process\
  \ text for consistency in encoding and punctuation, and to manage expectations for\
  \ questions on topics outside the training data.\n        "
model-index:
- name: roberta-finetuned
  results:
  - task:
      type: question-answering
    dataset:
      name: SQuAD v2
      type: squad_v2
    metrics:
    - type: Exact
      value: 100.0
    - type: F1
      value: 100.0
    - type: Total
      value: 2
    - type: Hasans Exact
      value: 100.0
    - type: Hasans F1
      value: 100.0
    - type: Hasans Total
      value: 2
    - type: Best Exact
      value: 100.0
    - type: Best Exact Thresh
      value: 0.9603068232536316
    - type: Best F1
      value: 100.0
    - type: Best F1 Thresh
      value: 0.9603068232536316
    - type: Total Time In Seconds
      value: 0.034724613000435056
    - type: Samples Per Second
      value: 57.59603425889707
    - type: Latency In Seconds
      value: 0.017362306500217528
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** mit
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]