harshita23sh commited on
Commit
80d017e
1 Parent(s): 2cda647

Add SetFit model

Browse files
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 768,
3
+ "pooling_mode_cls_token": false,
4
+ "pooling_mode_mean_tokens": true,
5
+ "pooling_mode_max_tokens": false,
6
+ "pooling_mode_mean_sqrt_len_tokens": false,
7
+ "pooling_mode_weightedmean_tokens": false,
8
+ "pooling_mode_lasttoken": false,
9
+ "include_prompt": true
10
+ }
README.md ADDED
@@ -0,0 +1,204 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: setfit
3
+ tags:
4
+ - setfit
5
+ - sentence-transformers
6
+ - text-classification
7
+ - generated_from_setfit_trainer
8
+ base_model: sentence-transformers/paraphrase-mpnet-base-v2
9
+ metrics:
10
+ - accuracy
11
+ widget:
12
+ - text: I need to repair an issue I found in my policy.
13
+ - text: I'm wondering about the developments in my policy alteration.
14
+ - text: Hi, I've noticed my policy is incomplete and needs additional details.
15
+ - text: What's the latest on the status of my claim?
16
+ - text: I have my own health insurance policy.
17
+ pipeline_tag: text-classification
18
+ inference: true
19
+ model-index:
20
+ - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
21
+ results:
22
+ - task:
23
+ type: text-classification
24
+ name: Text Classification
25
+ dataset:
26
+ name: Unknown
27
+ type: unknown
28
+ split: test
29
+ metrics:
30
+ - type: accuracy
31
+ value: 0.9159663865546218
32
+ name: Accuracy
33
+ ---
34
+
35
+ # SetFit with sentence-transformers/paraphrase-mpnet-base-v2
36
+
37
+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
38
+
39
+ The model has been trained using an efficient few-shot learning technique that involves:
40
+
41
+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
42
+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
43
+
44
+ ## Model Details
45
+
46
+ ### Model Description
47
+ - **Model Type:** SetFit
48
+ - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
49
+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
50
+ - **Maximum Sequence Length:** 512 tokens
51
+ - **Number of Classes:** 5 classes
52
+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
53
+ <!-- - **Language:** Unknown -->
54
+ <!-- - **License:** Unknown -->
55
+
56
+ ### Model Sources
57
+
58
+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
59
+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
60
+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
61
+
62
+ ### Model Labels
63
+ | Label | Examples |
64
+ |:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
65
+ | 2 | <ul><li>'I require assistance in altering certain elements of my policy.'</li><li>"Hey there, I've spotted a gap in my policy information."</li><li>'I need to rectify something within my policy documentation.'</li></ul> |
66
+ | 4 | <ul><li>"I am covered by health insurance through my employer's sponsorship."</li><li>'Is it permissible to transfer my health plan to ACKO?'</li><li>'My old health policy from another insurance provider is no longer in effect.'</li></ul> |
67
+ | 3 | <ul><li>'Can you reveal all policies under my profile?'</li><li>'I want to be informed about the status of all my insurance arrangements.'</li><li>"Is it possible for you to display my family's health insurance policies?"</li></ul> |
68
+ | 1 | <ul><li>'How is my vehicle claim proceeding?'</li><li>"I'm curious about the status of my car insurance claim."</li><li>'Am I required to provide additional evidence for my claims?'</li></ul> |
69
+ | 0 | <ul><li>'I need help selecting an appropriate health insurance plan for my family.'</li><li>"I'm looking for a health policy that will cover me along with my two kids."</li><li>"I'm in urgent need of a health insurance plan for my family's wellbeing."</li></ul> |
70
+
71
+ ## Evaluation
72
+
73
+ ### Metrics
74
+ | Label | Accuracy |
75
+ |:--------|:---------|
76
+ | **all** | 0.9160 |
77
+
78
+ ## Uses
79
+
80
+ ### Direct Use for Inference
81
+
82
+ First install the SetFit library:
83
+
84
+ ```bash
85
+ pip install setfit
86
+ ```
87
+
88
+ Then you can load this model and run inference.
89
+
90
+ ```python
91
+ from setfit import SetFitModel
92
+
93
+ # Download from the 🤗 Hub
94
+ model = SetFitModel.from_pretrained("harshita23sh/setfit-model-intent-classification-insurance")
95
+ # Run inference
96
+ preds = model("I have my own health insurance policy.")
97
+ ```
98
+
99
+ <!--
100
+ ### Downstream Use
101
+
102
+ *List how someone could finetune this model on their own dataset.*
103
+ -->
104
+
105
+ <!--
106
+ ### Out-of-Scope Use
107
+
108
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
109
+ -->
110
+
111
+ <!--
112
+ ## Bias, Risks and Limitations
113
+
114
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
115
+ -->
116
+
117
+ <!--
118
+ ### Recommendations
119
+
120
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
121
+ -->
122
+
123
+ ## Training Details
124
+
125
+ ### Training Set Metrics
126
+ | Training set | Min | Median | Max |
127
+ |:-------------|:----|:-------|:----|
128
+ | Word count | 5 | 10.6 | 15 |
129
+
130
+ | Label | Training Sample Count |
131
+ |:------|:----------------------|
132
+ | 0 | 5 |
133
+ | 1 | 8 |
134
+ | 2 | 12 |
135
+ | 3 | 4 |
136
+ | 4 | 11 |
137
+
138
+ ### Training Hyperparameters
139
+ - batch_size: (16, 16)
140
+ - num_epochs: (1, 1)
141
+ - max_steps: -1
142
+ - sampling_strategy: oversampling
143
+ - num_iterations: 20
144
+ - body_learning_rate: (2e-05, 2e-05)
145
+ - head_learning_rate: 2e-05
146
+ - loss: CosineSimilarityLoss
147
+ - distance_metric: cosine_distance
148
+ - margin: 0.25
149
+ - end_to_end: False
150
+ - use_amp: False
151
+ - warmup_proportion: 0.1
152
+ - seed: 42
153
+ - eval_max_steps: -1
154
+ - load_best_model_at_end: False
155
+
156
+ ### Training Results
157
+ | Epoch | Step | Training Loss | Validation Loss |
158
+ |:-----:|:----:|:-------------:|:---------------:|
159
+ | 0.01 | 1 | 0.1388 | - |
160
+ | 0.5 | 50 | 0.0087 | - |
161
+ | 1.0 | 100 | 0.0029 | - |
162
+
163
+ ### Framework Versions
164
+ - Python: 3.10.12
165
+ - SetFit: 1.0.3
166
+ - Sentence Transformers: 2.7.0
167
+ - Transformers: 4.40.0
168
+ - PyTorch: 2.2.1+cu121
169
+ - Datasets: 2.19.0
170
+ - Tokenizers: 0.19.1
171
+
172
+ ## Citation
173
+
174
+ ### BibTeX
175
+ ```bibtex
176
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
177
+ doi = {10.48550/ARXIV.2209.11055},
178
+ url = {https://arxiv.org/abs/2209.11055},
179
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
180
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
181
+ title = {Efficient Few-Shot Learning Without Prompts},
182
+ publisher = {arXiv},
183
+ year = {2022},
184
+ copyright = {Creative Commons Attribution 4.0 International}
185
+ }
186
+ ```
187
+
188
+ <!--
189
+ ## Glossary
190
+
191
+ *Clearly define terms in order to be accessible across audiences.*
192
+ -->
193
+
194
+ <!--
195
+ ## Model Card Authors
196
+
197
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
198
+ -->
199
+
200
+ <!--
201
+ ## Model Card Contact
202
+
203
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
204
+ -->
config.json ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "sentence-transformers/paraphrase-mpnet-base-v2",
3
+ "architectures": [
4
+ "MPNetModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": 0,
8
+ "eos_token_id": 2,
9
+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
11
+ "hidden_size": 768,
12
+ "initializer_range": 0.02,
13
+ "intermediate_size": 3072,
14
+ "layer_norm_eps": 1e-05,
15
+ "max_position_embeddings": 514,
16
+ "model_type": "mpnet",
17
+ "num_attention_heads": 12,
18
+ "num_hidden_layers": 12,
19
+ "pad_token_id": 1,
20
+ "relative_attention_num_buckets": 32,
21
+ "torch_dtype": "float32",
22
+ "transformers_version": "4.40.0",
23
+ "vocab_size": 30527
24
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "2.0.0",
4
+ "transformers": "4.7.0",
5
+ "pytorch": "1.9.0+cu102"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null
9
+ }
config_setfit.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "normalize_embeddings": false,
3
+ "labels": null
4
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e7f4b04ac23706d4ab83be33d9a26b0b75b9f37afcdd222bcfa1305ef651cad0
3
+ size 437967672
model_head.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7e9086126b2e309dfecb86b8bca7283adb0d8d95c4deabd9c854d8dddf76921c
3
+ size 31647
modules.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "idx": 0,
4
+ "name": "0",
5
+ "path": "",
6
+ "type": "sentence_transformers.models.Transformer"
7
+ },
8
+ {
9
+ "idx": 1,
10
+ "name": "1",
11
+ "path": "1_Pooling",
12
+ "type": "sentence_transformers.models.Pooling"
13
+ }
14
+ ]
sentence_bert_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "max_seq_length": 512,
3
+ "do_lower_case": false
4
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<s>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "cls_token": {
10
+ "content": "<s>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "eos_token": {
17
+ "content": "</s>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "mask_token": {
24
+ "content": "<mask>",
25
+ "lstrip": true,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ },
30
+ "pad_token": {
31
+ "content": "<pad>",
32
+ "lstrip": false,
33
+ "normalized": false,
34
+ "rstrip": false,
35
+ "single_word": false
36
+ },
37
+ "sep_token": {
38
+ "content": "</s>",
39
+ "lstrip": false,
40
+ "normalized": false,
41
+ "rstrip": false,
42
+ "single_word": false
43
+ },
44
+ "unk_token": {
45
+ "content": "[UNK]",
46
+ "lstrip": false,
47
+ "normalized": false,
48
+ "rstrip": false,
49
+ "single_word": false
50
+ }
51
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "<s>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "1": {
12
+ "content": "<pad>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "</s>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "104": {
28
+ "content": "[UNK]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "30526": {
36
+ "content": "<mask>",
37
+ "lstrip": true,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "bos_token": "<s>",
45
+ "clean_up_tokenization_spaces": true,
46
+ "cls_token": "<s>",
47
+ "do_basic_tokenize": true,
48
+ "do_lower_case": true,
49
+ "eos_token": "</s>",
50
+ "mask_token": "<mask>",
51
+ "model_max_length": 512,
52
+ "never_split": null,
53
+ "pad_token": "<pad>",
54
+ "sep_token": "</s>",
55
+ "strip_accents": null,
56
+ "tokenize_chinese_chars": true,
57
+ "tokenizer_class": "MPNetTokenizer",
58
+ "unk_token": "[UNK]"
59
+ }
vocab.txt ADDED
The diff for this file is too large to render. See raw diff