Hugging Face's logo

vtiyyal1
/
quality_model_apr3

Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Model card Files
xet
 Community
vtiyyal1 commited on
Commit
b7b2993
·
verified ·
1 Parent(s): 7776dfd

Training completed!

Browse files
Files changed (2) hide show
  1. README.md +112 -0
  2. model.safetensors +1 -1
README.md ADDED
@@ -0,0 +1,112 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ base_model: distilbert-base-uncased
4
+ tags:
5
+ - generated_from_trainer
6
+ model-index:
7
+ - name: quality_model_apr3
8
+ results: []
9
+ ---
10
+
11
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
12
+ should probably proofread and complete it, then remove this comment. -->
13
+
14
+ # quality_model_apr3
15
+
16
+ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
17
+ It achieves the following results on the evaluation set:
18
+ - Loss: 0.0117
19
+ - Mse: 0.0117
20
+
21
+ ## Model description
22
+
23
+ More information needed
24
+
25
+ ## Intended uses & limitations
26
+
27
+ More information needed
28
+
29
+ ## Training and evaluation data
30
+
31
+ More information needed
32
+
33
+ ## Training procedure
34
+
35
+ ### Training hyperparameters
36
+
37
+ The following hyperparameters were used during training:
38
+ - learning_rate: 5e-05
39
+ - train_batch_size: 8
40
+ - eval_batch_size: 8
41
+ - seed: 42
42
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
43
+ - lr_scheduler_type: linear
44
+ - num_epochs: 3
45
+
46
+ ### Training results
47
+
48
+ | Training Loss | Epoch | Step | Validation Loss | Mse |
49
+ |:-------------:|:-----:|:----:|:---------------:|:------:|
50
+ | 0.0209 | 0.05 | 50 | 0.0135 | 0.0135 |
51
+ | 0.0179 | 0.11 | 100 | 0.0118 | 0.0118 |
52
+ | 0.0153 | 0.16 | 150 | 0.0116 | 0.0116 |
53
+ | 0.0159 | 0.22 | 200 | 0.0131 | 0.0131 |
54
+ | 0.0169 | 0.27 | 250 | 0.0163 | 0.0163 |
55
+ | 0.0116 | 0.32 | 300 | 0.0116 | 0.0116 |
56
+ | 0.0094 | 0.38 | 350 | 0.0123 | 0.0123 |
57
+ | 0.0168 | 0.43 | 400 | 0.0115 | 0.0115 |
58
+ | 0.0224 | 0.48 | 450 | 0.0135 | 0.0135 |
59
+ | 0.0144 | 0.54 | 500 | 0.0116 | 0.0116 |
60
+ | 0.0147 | 0.59 | 550 | 0.0115 | 0.0115 |
61
+ | 0.0117 | 0.65 | 600 | 0.0121 | 0.0121 |
62
+ | 0.0198 | 0.7 | 650 | 0.0120 | 0.0120 |
63
+ | 0.0119 | 0.75 | 700 | 0.0121 | 0.0121 |
64
+ | 0.0166 | 0.81 | 750 | 0.0118 | 0.0118 |
65
+ | 0.0096 | 0.86 | 800 | 0.0123 | 0.0123 |
66
+ | 0.0166 | 0.92 | 850 | 0.0115 | 0.0115 |
67
+ | 0.0181 | 0.97 | 900 | 0.0114 | 0.0114 |
68
+ | 0.0128 | 1.02 | 950 | 0.0114 | 0.0114 |
69
+ | 0.0174 | 1.08 | 1000 | 0.0113 | 0.0113 |
70
+ | 0.0161 | 1.13 | 1050 | 0.0126 | 0.0126 |
71
+ | 0.0174 | 1.19 | 1100 | 0.0141 | 0.0141 |
72
+ | 0.016 | 1.24 | 1150 | 0.0114 | 0.0114 |
73
+ | 0.0098 | 1.29 | 1200 | 0.0114 | 0.0114 |
74
+ | 0.0179 | 1.35 | 1250 | 0.0126 | 0.0126 |
75
+ | 0.0141 | 1.4 | 1300 | 0.0115 | 0.0115 |
76
+ | 0.0118 | 1.45 | 1350 | 0.0116 | 0.0116 |
77
+ | 0.0115 | 1.51 | 1400 | 0.0113 | 0.0113 |
78
+ | 0.0118 | 1.56 | 1450 | 0.0113 | 0.0113 |
79
+ | 0.0165 | 1.62 | 1500 | 0.0118 | 0.0118 |
80
+ | 0.0129 | 1.67 | 1550 | 0.0113 | 0.0113 |
81
+ | 0.011 | 1.72 | 1600 | 0.0118 | 0.0118 |
82
+ | 0.0128 | 1.78 | 1650 | 0.0120 | 0.0120 |
83
+ | 0.0145 | 1.83 | 1700 | 0.0124 | 0.0124 |
84
+ | 0.014 | 1.89 | 1750 | 0.0114 | 0.0114 |
85
+ | 0.0155 | 1.94 | 1800 | 0.0114 | 0.0114 |
86
+ | 0.0144 | 1.99 | 1850 | 0.0114 | 0.0114 |
87
+ | 0.0141 | 2.05 | 1900 | 0.0114 | 0.0114 |
88
+ | 0.0108 | 2.1 | 1950 | 0.0117 | 0.0117 |
89
+ | 0.0109 | 2.16 | 2000 | 0.0113 | 0.0113 |
90
+ | 0.0124 | 2.21 | 2050 | 0.0132 | 0.0132 |
91
+ | 0.0169 | 2.26 | 2100 | 0.0123 | 0.0123 |
92
+ | 0.0115 | 2.32 | 2150 | 0.0120 | 0.0120 |
93
+ | 0.0102 | 2.37 | 2200 | 0.0117 | 0.0117 |
94
+ | 0.0189 | 2.42 | 2250 | 0.0116 | 0.0116 |
95
+ | 0.0136 | 2.48 | 2300 | 0.0115 | 0.0115 |
96
+ | 0.0116 | 2.53 | 2350 | 0.0119 | 0.0119 |
97
+ | 0.0141 | 2.59 | 2400 | 0.0119 | 0.0119 |
98
+ | 0.0098 | 2.64 | 2450 | 0.0120 | 0.0120 |
99
+ | 0.0081 | 2.69 | 2500 | 0.0117 | 0.0117 |
100
+ | 0.009 | 2.75 | 2550 | 0.0119 | 0.0119 |
101
+ | 0.0121 | 2.8 | 2600 | 0.0118 | 0.0118 |
102
+ | 0.0128 | 2.86 | 2650 | 0.0123 | 0.0123 |
103
+ | 0.0131 | 2.91 | 2700 | 0.0117 | 0.0117 |
104
+ | 0.009 | 2.96 | 2750 | 0.0117 | 0.0117 |
105
+
106
+
107
+ ### Framework versions
108
+
109
+ - Transformers 4.39.3
110
+ - Pytorch 2.2.1+cu121
111
+ - Datasets 2.18.0
112
+ - Tokenizers 0.15.2
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:9f6e3075f5c33306446dcbf1334d713b6986520f89f52c764ca34bd4c9594450
3
  size 267829484
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5cf392e8928c3534061a6ba5c4026978edc8fd6a51ebef7a37b499a2dbf0e2cb
3
  size 267829484