Ramyashree commited on
Commit
ea40046
1 Parent(s): b299dd4

Add SetFit model

Browse files
1_Pooling/config.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
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
+ }
README.md ADDED
@@ -0,0 +1,203 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: setfit
3
+ tags:
4
+ - setfit
5
+ - sentence-transformers
6
+ - text-classification
7
+ - generated_from_setfit_trainer
8
+ datasets:
9
+ - SetFit/SentEval-CR
10
+ metrics:
11
+ - accuracy
12
+ widget:
13
+ - text: you can take pic of your friends and the picture will pop up when they call
14
+ .
15
+ - text: the speakerphone , the radio , all features work perfectly .
16
+ - text: 'a ) the picture quality ( color and sharpness of focusing ) are so great
17
+ , it completely eliminated my doubt about digital imaging -- - how could one eat
18
+ rice one grain at a time : - ) )'
19
+ - text: so far the dvd works so i hope it does n 't break down like the reviews i
20
+ 've read .
21
+ - text: i have a couple hundred contacts and the menu loads within a few seconds ,
22
+ no big deal .
23
+ pipeline_tag: text-classification
24
+ inference: true
25
+ base_model: sentence-transformers/paraphrase-mpnet-base-v2
26
+ model-index:
27
+ - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
28
+ results:
29
+ - task:
30
+ type: text-classification
31
+ name: Text Classification
32
+ dataset:
33
+ name: SetFit/SentEval-CR
34
+ type: SetFit/SentEval-CR
35
+ split: test
36
+ metrics:
37
+ - type: accuracy
38
+ value: 0.8698539176626826
39
+ name: Accuracy
40
+ ---
41
+
42
+ # SetFit with sentence-transformers/paraphrase-mpnet-base-v2
43
+
44
+ This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [SetFit/SentEval-CR](https://huggingface.co/datasets/SetFit/SentEval-CR) dataset 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.
45
+
46
+ The model has been trained using an efficient few-shot learning technique that involves:
47
+
48
+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
49
+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
50
+
51
+ ## Model Details
52
+
53
+ ### Model Description
54
+ - **Model Type:** SetFit
55
+ - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
56
+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
57
+ - **Maximum Sequence Length:** 512 tokens
58
+ - **Number of Classes:** 2 classes
59
+ - **Training Dataset:** [SetFit/SentEval-CR](https://huggingface.co/datasets/SetFit/SentEval-CR)
60
+ <!-- - **Language:** Unknown -->
61
+ <!-- - **License:** Unknown -->
62
+
63
+ ### Model Sources
64
+
65
+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
66
+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
67
+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
68
+
69
+ ### Model Labels
70
+ | Label | Examples |
71
+ |:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
72
+ | 1 | <ul><li>'* slick-looking design and improved interface'</li><li>'as for bluetooth , no problems at all .'</li><li>'2 ) storage capacity'</li></ul> |
73
+ | 0 | <ul><li>"the day finally arrived when i was sure i 'd leave sprint ."</li><li>"neither message was answered ( they ask for 24 hours before replying - i 've been waiting 27 days . )"</li><li>'only problem is that is a bit heavy .'</li></ul> |
74
+
75
+ ## Evaluation
76
+
77
+ ### Metrics
78
+ | Label | Accuracy |
79
+ |:--------|:---------|
80
+ | **all** | 0.8699 |
81
+
82
+ ## Uses
83
+
84
+ ### Direct Use for Inference
85
+
86
+ First install the SetFit library:
87
+
88
+ ```bash
89
+ pip install setfit
90
+ ```
91
+
92
+ Then you can load this model and run inference.
93
+
94
+ ```python
95
+ from setfit import SetFitModel
96
+
97
+ # Download from the 🤗 Hub
98
+ model = SetFitModel.from_pretrained("Ramyashree/setfit-trained-model")
99
+ # Run inference
100
+ preds = model("the speakerphone , the radio , all features work perfectly .")
101
+ ```
102
+
103
+ <!--
104
+ ### Downstream Use
105
+
106
+ *List how someone could finetune this model on their own dataset.*
107
+ -->
108
+
109
+ <!--
110
+ ### Out-of-Scope Use
111
+
112
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
113
+ -->
114
+
115
+ <!--
116
+ ## Bias, Risks and Limitations
117
+
118
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
119
+ -->
120
+
121
+ <!--
122
+ ### Recommendations
123
+
124
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
125
+ -->
126
+
127
+ ## Training Details
128
+
129
+ ### Training Set Metrics
130
+ | Training set | Min | Median | Max |
131
+ |:-------------|:----|:--------|:----|
132
+ | Word count | 4 | 18.0625 | 44 |
133
+
134
+ | Label | Training Sample Count |
135
+ |:------|:----------------------|
136
+ | 0 | 7 |
137
+ | 1 | 9 |
138
+
139
+ ### Training Hyperparameters
140
+ - batch_size: (16, 16)
141
+ - num_epochs: (1, 1)
142
+ - max_steps: -1
143
+ - sampling_strategy: oversampling
144
+ - num_iterations: 20
145
+ - body_learning_rate: (2e-05, 2e-05)
146
+ - head_learning_rate: 2e-05
147
+ - loss: CosineSimilarityLoss
148
+ - distance_metric: cosine_distance
149
+ - margin: 0.25
150
+ - end_to_end: False
151
+ - use_amp: False
152
+ - warmup_proportion: 0.1
153
+ - seed: 42
154
+ - eval_max_steps: -1
155
+ - load_best_model_at_end: False
156
+
157
+ ### Training Results
158
+ | Epoch | Step | Training Loss | Validation Loss |
159
+ |:-----:|:----:|:-------------:|:---------------:|
160
+ | 0.025 | 1 | 0.2289 | - |
161
+
162
+ ### Framework Versions
163
+ - Python: 3.10.12
164
+ - SetFit: 1.0.1
165
+ - Sentence Transformers: 2.2.2
166
+ - Transformers: 4.35.2
167
+ - PyTorch: 2.1.0+cu121
168
+ - Datasets: 2.15.0
169
+ - Tokenizers: 0.15.0
170
+
171
+ ## Citation
172
+
173
+ ### BibTeX
174
+ ```bibtex
175
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
176
+ doi = {10.48550/ARXIV.2209.11055},
177
+ url = {https://arxiv.org/abs/2209.11055},
178
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
179
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
180
+ title = {Efficient Few-Shot Learning Without Prompts},
181
+ publisher = {arXiv},
182
+ year = {2022},
183
+ copyright = {Creative Commons Attribution 4.0 International}
184
+ }
185
+ ```
186
+
187
+ <!--
188
+ ## Glossary
189
+
190
+ *Clearly define terms in order to be accessible across audiences.*
191
+ -->
192
+
193
+ <!--
194
+ ## Model Card Authors
195
+
196
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
197
+ -->
198
+
199
+ <!--
200
+ ## Model Card Contact
201
+
202
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
203
+ -->
config.json ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "/root/.cache/torch/sentence_transformers/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": "4.35.2",
23
+ "vocab_size": 30527
24
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "2.0.0",
4
+ "transformers": "4.7.0",
5
+ "pytorch": "1.9.0+cu102"
6
+ }
7
+ }
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 sha256:548d116eacf8249124debb645e53dd90266684b6d7994e95d259277e6b6ce536
3
+ size 437967672
model_head.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:13f111247ebd691bb3e49fae47f52295476b6ab26a253c6dfdccb5a0bed74949
3
+ size 6991
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": true,
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": true,
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