Hugging Face's logo

ilyankou
/
spatial-classifier

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Model card Files
xet
Community
ilyankou commited on
Commit
0a6c7fc
·
verified ·
1 Parent(s): 240a355

Upload v0.1

Browse files
Files changed (3) hide show
  1. README.md +46 -78
  2. model.safetensors +1 -1
  3. model_head.pkl +0 -0
README.md CHANGED
@@ -5,35 +5,17 @@ tags:
5
  - text-classification
6
  - generated_from_setfit_trainer
7
  widget:
8
- - text: what is the climate in all of greece
9
- - text: what makes plants greener
10
- - text: how old is jacob sar
11
- - text: how do i insert a pdf file into the body of an e-mail
12
- - text: low carb how many grams
13
  metrics:
14
  - accuracy
15
- - f1
16
  pipeline_tag: text-classification
17
  library_name: setfit
18
  inference: true
19
  base_model: BAAI/bge-small-en-v1.5
20
- model-index:
21
- - name: SetFit with BAAI/bge-small-en-v1.5
22
- results:
23
- - task:
24
- type: text-classification
25
- name: Text Classification
26
- dataset:
27
- name: Unknown
28
- type: unknown
29
- split: test
30
- metrics:
31
- - type: accuracy
32
- value: 0.9864864864864865
33
- name: Accuracy
34
- - type: f1
35
- value: 0.9861111111111112
36
- name: F1
37
  ---
38
 
39
  # SetFit with BAAI/bge-small-en-v1.5
@@ -69,13 +51,6 @@ The model has been trained using an efficient few-shot learning technique that i
69
  | 1 | <ul><li>'how far is palms casino from the airport in las vegas'</li><li>'anarkali bazar lahore'</li><li>'what county is alma nebraska in?'</li></ul> |
70
  | 0 | <ul><li>'what is symptom of bipolar disorder'</li><li>'early symptoms of shingles outbreak'</li><li>'bnsf total employees'</li></ul> |
71
 
72
- ## Evaluation
73
-
74
- ### Metrics
75
- | Label | Accuracy | F1 |
76
- |:--------|:---------|:-------|
77
- | **all** | 0.9865 | 0.9861 |
78
-
79
  ## Uses
80
 
81
  ### Direct Use for Inference
@@ -94,7 +69,7 @@ from setfit import SetFitModel
94
  # Download from the 🤗 Hub
95
  model = SetFitModel.from_pretrained("setfit_model_id")
96
  # Run inference
97
- preds = model("how old is jacob sar")
98
  ```
99
 
100
  <!--
@@ -126,12 +101,12 @@ preds = model("how old is jacob sar")
126
  ### Training Set Metrics
127
  | Training set | Min | Median | Max |
128
  |:-------------|:----|:-------|:----|
129
- | Word count | 2 | 6.2787 | 21 |
130
 
131
  | Label | Training Sample Count |
132
  |:------|:----------------------|
133
- | 0 | 603 |
134
- | 1 | 574 |
135
 
136
  ### Training Hyperparameters
137
  - batch_size: (64, 64)
@@ -149,52 +124,45 @@ preds = model("how old is jacob sar")
149
  - l2_weight: 0.01
150
  - seed: 42
151
  - eval_max_steps: -1
152
- - load_best_model_at_end: True
153
 
154
  ### Training Results
155
- | Epoch | Step | Training Loss | Validation Loss |
156
- |:------:|:----:|:-------------:|:---------------:|
157
- | 0.0001 | 1 | 0.238 | - |
158
- | 0.0046 | 50 | 0.2409 | - |
159
- | 0.0092 | 100 | 0.2367 | - |
160
- | 0.0138 | 150 | 0.2297 | - |
161
- | 0.0184 | 200 | 0.2227 | - |
162
- | 0.0230 | 250 | 0.2005 | - |
163
- | 0.0277 | 300 | 0.1596 | - |
164
- | 0.0323 | 350 | 0.0969 | - |
165
- | 0.0369 | 400 | 0.0633 | - |
166
- | 0.0415 | 450 | 0.0385 | - |
167
- | 0.0461 | 500 | 0.02 | 0.0571 |
168
- | 0.0507 | 550 | 0.0125 | - |
169
- | 0.0553 | 600 | 0.0089 | - |
170
- | 0.0599 | 650 | 0.0049 | - |
171
- | 0.0645 | 700 | 0.0037 | - |
172
- | 0.0691 | 750 | 0.0032 | - |
173
- | 0.0737 | 800 | 0.0023 | - |
174
- | 0.0784 | 850 | 0.0021 | - |
175
- | 0.0830 | 900 | 0.002 | - |
176
- | 0.0876 | 950 | 0.0017 | - |
177
- | 0.0922 | 1000 | 0.0014 | 0.0617 |
178
- | 0.0968 | 1050 | 0.0013 | - |
179
- | 0.1014 | 1100 | 0.0012 | - |
180
- | 0.1060 | 1150 | 0.0011 | - |
181
- | 0.1106 | 1200 | 0.001 | - |
182
- | 0.1152 | 1250 | 0.0011 | - |
183
- | 0.1198 | 1300 | 0.0013 | - |
184
- | 0.1244 | 1350 | 0.0012 | - |
185
- | 0.1291 | 1400 | 0.0008 | - |
186
- | 0.1337 | 1450 | 0.0008 | - |
187
- | 0.1383 | 1500 | 0.0008 | 0.0688 |
188
- | 0.1429 | 1550 | 0.0007 | - |
189
- | 0.1475 | 1600 | 0.0007 | - |
190
- | 0.1521 | 1650 | 0.0007 | - |
191
- | 0.1567 | 1700 | 0.0006 | - |
192
- | 0.1613 | 1750 | 0.0009 | - |
193
- | 0.1659 | 1800 | 0.0007 | - |
194
- | 0.1705 | 1850 | 0.0006 | - |
195
- | 0.1751 | 1900 | 0.0006 | - |
196
- | 0.1798 | 1950 | 0.0006 | - |
197
- | 0.1844 | 2000 | 0.0005 | 0.0663 |
198
 
199
  ### Framework Versions
200
  - Python: 3.11.5
 
5
  - text-classification
6
  - generated_from_setfit_trainer
7
  widget:
8
+ - text: hotel in geneva airport
9
+ - text: what payroll deduction is mpp
10
+ - text: weather in erlanger ky
11
+ - text: what is the coordinates of point p
12
+ - text: what's the weather in roseburg
13
  metrics:
14
  - accuracy
 
15
  pipeline_tag: text-classification
16
  library_name: setfit
17
  inference: true
18
  base_model: BAAI/bge-small-en-v1.5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
19
  ---
20
 
21
  # SetFit with BAAI/bge-small-en-v1.5
 
51
  | 1 | <ul><li>'how far is palms casino from the airport in las vegas'</li><li>'anarkali bazar lahore'</li><li>'what county is alma nebraska in?'</li></ul> |
52
  | 0 | <ul><li>'what is symptom of bipolar disorder'</li><li>'early symptoms of shingles outbreak'</li><li>'bnsf total employees'</li></ul> |
53
 
 
 
 
 
 
 
 
54
  ## Uses
55
 
56
  ### Direct Use for Inference
 
69
  # Download from the 🤗 Hub
70
  model = SetFitModel.from_pretrained("setfit_model_id")
71
  # Run inference
72
+ preds = model("weather in erlanger ky")
73
  ```
74
 
75
  <!--
 
101
  ### Training Set Metrics
102
  | Training set | Min | Median | Max |
103
  |:-------------|:----|:-------|:----|
104
+ | Word count | 2 | 6.3028 | 21 |
105
 
106
  | Label | Training Sample Count |
107
  |:------|:----------------------|
108
+ | 0 | 755 |
109
+ | 1 | 718 |
110
 
111
  ### Training Hyperparameters
112
  - batch_size: (64, 64)
 
124
  - l2_weight: 0.01
125
  - seed: 42
126
  - eval_max_steps: -1
127
+ - load_best_model_at_end: False
128
 
129
  ### Training Results
130
+ | Epoch | Step | Training Loss | Validation Loss |
131
+ |:------:|:-----:|:-------------:|:---------------:|
132
+ | 0.0001 | 1 | 0.2507 | - |
133
+ | 0.0294 | 500 | 0.1803 | - |
134
+ | 0.0589 | 1000 | 0.0135 | - |
135
+ | 0.0883 | 1500 | 0.0021 | - |
136
+ | 0.1178 | 2000 | 0.001 | - |
137
+ | 0.1472 | 2500 | 0.0007 | - |
138
+ | 0.1766 | 3000 | 0.0005 | - |
139
+ | 0.2061 | 3500 | 0.0004 | - |
140
+ | 0.2355 | 4000 | 0.0004 | - |
141
+ | 0.2649 | 4500 | 0.0003 | - |
142
+ | 0.2944 | 5000 | 0.0003 | - |
143
+ | 0.3238 | 5500 | 0.0003 | - |
144
+ | 0.3533 | 6000 | 0.0003 | - |
145
+ | 0.3827 | 6500 | 0.0002 | - |
146
+ | 0.4121 | 7000 | 0.0003 | - |
147
+ | 0.4416 | 7500 | 0.0002 | - |
148
+ | 0.4710 | 8000 | 0.0002 | - |
149
+ | 0.5004 | 8500 | 0.0002 | - |
150
+ | 0.5299 | 9000 | 0.0002 | - |
151
+ | 0.5593 | 9500 | 0.0002 | - |
152
+ | 0.5888 | 10000 | 0.0002 | - |
153
+ | 0.6182 | 10500 | 0.0002 | - |
154
+ | 0.6476 | 11000 | 0.0001 | - |
155
+ | 0.6771 | 11500 | 0.0001 | - |
156
+ | 0.7065 | 12000 | 0.0001 | - |
157
+ | 0.7359 | 12500 | 0.0001 | - |
158
+ | 0.7654 | 13000 | 0.0001 | - |
159
+ | 0.7948 | 13500 | 0.0001 | - |
160
+ | 0.8243 | 14000 | 0.0001 | - |
161
+ | 0.8537 | 14500 | 0.0001 | - |
162
+ | 0.8831 | 15000 | 0.0001 | - |
163
+ | 0.9126 | 15500 | 0.0001 | - |
164
+ | 0.9420 | 16000 | 0.0001 | - |
165
+ | 0.9714 | 16500 | 0.0001 | - |
 
 
 
 
 
 
 
166
 
167
  ### Framework Versions
168
  - Python: 3.11.5
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:97ded2ffd417d29f71a843cb518865491eae911810d4c7228eba0eb4fe818335
3
  size 133462128
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:487f5a8abf3dfc01c20f682fea8260245f8d65dc1c360034de899a102e8e271f
3
  size 133462128
model_head.pkl CHANGED
Binary files a/model_head.pkl and b/model_head.pkl differ