File size: 5,750 Bytes
72949f0
c799181
 
 
 
889dcea
c799181
efbd6cd
 
 
 
 
 
 
 
 
 
 
 
4ef9a81
 
 
ce837db
 
 
e74c4d2
 
 
70de211
 
 
4a59a52
 
 
20ffa7b
 
 
60a79a4
 
 
ccb1ef0
 
 
72949f0
 
c799181
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
---
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilbert-finetuned-uncased-squad_v2
  results:
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: SQuAD v2
      type: squad_v2
      split: validation
    metrics:
    - type: exact
      value: 100.0
      name: Exact
    - type: f1
      value: 100.0
      name: F1
    - type: total
      value: 2
      name: Total
    - type: HasAns_exact
      value: 100.0
      name: Hasans_exact
    - type: HasAns_f1
      value: 100.0
      name: Hasans_f1
    - type: HasAns_total
      value: 2
      name: Hasans_total
    - type: best_exact
      value: 100.0
      name: Best_exact
    - type: best_exact_thresh
      value: 0.967875599861145
      name: Best_exact_thresh
    - type: best_f1
      value: 100.0
      name: Best_f1
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilbert-finetuned-uncased-squad_v2

This model was trained from scratch on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2617

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.6437        | 0.39  | 100  | 2.1780          |
| 2.1596        | 0.78  | 200  | 1.6557          |
| 1.8138        | 1.18  | 300  | 1.5683          |
| 1.6987        | 1.57  | 400  | 1.5076          |
| 1.6586        | 1.96  | 500  | 1.5350          |
| 1.5957        | 1.18  | 600  | 1.4431          |
| 1.5825        | 1.37  | 700  | 1.4955          |
| 1.5523        | 1.57  | 800  | 1.4444          |
| 1.5346        | 1.76  | 900  | 1.3930          |
| 1.5098        | 1.96  | 1000 | 1.4285          |
| 1.4632        | 2.16  | 1100 | 1.3630          |
| 1.4468        | 2.35  | 1200 | 1.3710          |
| 1.4343        | 2.55  | 1300 | 1.3422          |
| 1.4225        | 2.75  | 1400 | 1.3971          |
| 1.408         | 2.94  | 1500 | 1.4355          |
| 1.3609        | 3.14  | 1600 | 1.3332          |
| 1.3398        | 3.33  | 1700 | 1.3792          |
| 1.3224        | 3.53  | 1800 | 1.4172          |
| 1.3152        | 3.73  | 1900 | 1.3956          |
| 1.3141        | 3.92  | 2000 | 1.3748          |
| 1.3085        | 2.06  | 2100 | 1.3949          |
| 1.3325        | 2.16  | 2200 | 1.4870          |
| 1.3162        | 2.26  | 2300 | 1.4565          |
| 1.2936        | 2.35  | 2400 | 1.4496          |
| 1.2648        | 2.45  | 2500 | 1.2868          |
| 1.2531        | 2.55  | 2600 | 1.5094          |
| 1.2599        | 2.65  | 2700 | 1.3451          |
| 1.2545        | 2.75  | 2800 | 1.4071          |
| 1.2461        | 2.84  | 2900 | 1.3378          |
| 1.2038        | 2.94  | 3000 | 1.2946          |
| 1.1677        | 3.04  | 3100 | 1.4802          |
| 1.103         | 3.14  | 3200 | 1.3580          |
| 1.1205        | 3.24  | 3300 | 1.3819          |
| 1.095         | 3.33  | 3400 | 1.4336          |
| 1.0896        | 3.43  | 3500 | 1.4963          |
| 1.0856        | 3.53  | 3600 | 1.3384          |
| 1.0652        | 3.63  | 3700 | 1.3583          |
| 1.0859        | 3.73  | 3800 | 1.4140          |
| 1.058         | 3.83  | 3900 | 1.2617          |
| 1.0724        | 3.92  | 4000 | 1.3552          |
| 1.0509        | 4.02  | 4100 | 1.2971          |
| 0.97          | 4.12  | 4200 | 1.3268          |
| 0.95          | 4.22  | 4300 | 1.3754          |
| 0.9337        | 4.32  | 4400 | 1.3687          |
| 0.977         | 4.41  | 4500 | 1.3613          |
| 0.9484        | 4.51  | 4600 | 1.5139          |
| 0.9739        | 4.61  | 4700 | 1.2861          |
| 0.955         | 4.71  | 4800 | 1.3667          |
| 0.9536        | 4.81  | 4900 | 1.3180          |
| 0.9541        | 4.9   | 5000 | 1.4611          |
| 0.9462        | 5.0   | 5100 | 1.4067          |
| 0.8728        | 5.1   | 5200 | 1.3490          |
| 0.8646        | 5.2   | 5300 | 1.4631          |
| 0.8683        | 5.3   | 5400 | 1.4978          |
| 0.8571        | 5.39  | 5500 | 1.5814          |
| 0.8475        | 5.49  | 5600 | 1.5535          |
| 0.8653        | 5.59  | 5700 | 1.4938          |
| 0.8664        | 5.69  | 5800 | 1.4141          |
| 0.889         | 5.79  | 5900 | 1.4487          |
| 0.8601        | 5.88  | 6000 | 1.4722          |
| 0.8645        | 5.98  | 6100 | 1.5843          |
| 0.785         | 6.08  | 6200 | 1.6028          |
| 0.7711        | 6.18  | 6300 | 1.6271          |
| 0.8056        | 6.28  | 6400 | 1.5399          |
| 0.8087        | 6.37  | 6500 | 1.4927          |
| 0.7859        | 6.47  | 6600 | 1.4677          |
| 0.7896        | 6.57  | 6700 | 1.4780          |
| 0.7971        | 6.67  | 6800 | 1.5110          |
| 0.7952        | 6.77  | 6900 | 1.5459          |
| 0.7971        | 6.87  | 7000 | 1.5282          |
| 0.7908        | 6.96  | 7100 | 1.4799          |
| 0.7456        | 7.06  | 7200 | 1.6487          |
| 0.7236        | 7.16  | 7300 | 1.6543          |
| 0.7484        | 7.26  | 7400 | 1.6202          |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1