vgarg commited on
Commit
03e4bd1
1 Parent(s): 39a94ae

Add SetFit model

Browse files
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 1024,
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 +1,214 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: setfit
3
+ tags:
4
+ - setfit
5
+ - sentence-transformers
6
+ - text-classification
7
+ - generated_from_setfit_trainer
8
+ base_model: intfloat/multilingual-e5-large
9
+ metrics:
10
+ - accuracy
11
+ widget:
12
+ - text: What promotions in RTEC have shown declining effectiveness and can be discontinued?
13
+ - text: What are my priority brands in RTEC to get positive Lift Change in 2022?
14
+ - text: What would be the expected incremental volume lift if the discount on Brand
15
+ Zucaritas is raised by 5%?
16
+ - text: Which promotion types are better for low discounts for Zucaritas ?
17
+ - text: Which Promotions contributred the most ROI Change between 2022 and 2023?
18
+ pipeline_tag: text-classification
19
+ inference: true
20
+ model-index:
21
+ - name: SetFit with intfloat/multilingual-e5-large
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: 0.9714285714285714
33
+ name: Accuracy
34
+ ---
35
+
36
+ # SetFit with intfloat/multilingual-e5-large
37
+
38
+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) 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.
39
+
40
+ The model has been trained using an efficient few-shot learning technique that involves:
41
+
42
+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
43
+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
44
+
45
+ ## Model Details
46
+
47
+ ### Model Description
48
+ - **Model Type:** SetFit
49
+ - **Sentence Transformer body:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large)
50
+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
51
+ - **Maximum Sequence Length:** 512 tokens
52
+ - **Number of Classes:** 6 classes
53
+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
54
+ <!-- - **Language:** Unknown -->
55
+ <!-- - **License:** Unknown -->
56
+
57
+ ### Model Sources
58
+
59
+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
60
+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
61
+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
62
+
63
+ ### Model Labels
64
+ | Label | Examples |
65
+ |:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
66
+ | 2 | <ul><li>'Which brand has the highest change in lift for NATURAL JUICES category in 2022?'</li><li>'What are the main reasons for Lift decline for ULTRASTORE in 2023 compared to 2022?'</li><li>'Why has the overall Lift declined in 2023 in BREEZEFIZZ vs 2022?'</li></ul> |
67
+ | 5 | <ul><li>'How will the introduction of a 20% discount promotion for Rice Krispies in August affect incremental volume and ROI?'</li><li>'If I raise the discount by 20% on Brand BREEZEFIZZ, what will be the incremental roi?'</li><li>'How will increasing the discount by 50 percent on Brand BREEZEFIZZ affect the incremental volume lift?'</li></ul> |
68
+ | 1 | <ul><li>'How do the performance metrics of brands in the FIZZY DRINKS category compare to those in HYDRA and NATURAL JUICES concerning ROI change between 2021 to 2022?'</li><li>'Were there any sku-specific promotions that may have influenced their ROI and contributed to the overall decline?'</li><li>'Which category has contributed the most to ROI change between 2021 to 2022?'</li></ul> |
69
+ | 0 | <ul><li>'How is the promotion efficacy in 2022 compared to 2021 for CHEDRAUI channel?'</li><li>'Which subcategory have the highest ROI in 2022?'</li><li>'Which channel has the max ROI and Vol Lift when we run the Promotion for FIZZY DRINKS category?'</li></ul> |
70
+ | 3 | <ul><li>'Which promotion types are better for high discounts in Hydra category for 2022?'</li><li>'Which promotion types are preferable for high discounts in FIZZY DRINKS for CORN POPS?'</li><li>'Which promotion strategies in FIZZY DRINKS allow for offering substantial discounts while maintaining profitability?'</li></ul> |
71
+ | 4 | <ul><li>'Which promotions have scope for higher investment to drive more ROIs in Hydra ?'</li><li>'How can Hydra category investors diversify their investment portfolio to improve ROI?'</li><li>'For FIZZY DRINKS what would be a better investment strategy to gain ROI'</li></ul> |
72
+
73
+ ## Evaluation
74
+
75
+ ### Metrics
76
+ | Label | Accuracy |
77
+ |:--------|:---------|
78
+ | **all** | 0.9714 |
79
+
80
+ ## Uses
81
+
82
+ ### Direct Use for Inference
83
+
84
+ First install the SetFit library:
85
+
86
+ ```bash
87
+ pip install setfit
88
+ ```
89
+
90
+ Then you can load this model and run inference.
91
+
92
+ ```python
93
+ from setfit import SetFitModel
94
+
95
+ # Download from the 🤗 Hub
96
+ model = SetFitModel.from_pretrained("vgarg/promo_prescriptive_gpt_30_04_2024_v1")
97
+ # Run inference
98
+ preds = model("Which promotion types are better for low discounts for Zucaritas ?")
99
+ ```
100
+
101
+ <!--
102
+ ### Downstream Use
103
+
104
+ *List how someone could finetune this model on their own dataset.*
105
+ -->
106
+
107
+ <!--
108
+ ### Out-of-Scope Use
109
+
110
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
111
+ -->
112
+
113
+ <!--
114
+ ## Bias, Risks and Limitations
115
+
116
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
117
+ -->
118
+
119
+ <!--
120
+ ### Recommendations
121
+
122
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
123
+ -->
124
+
125
+ ## Training Details
126
+
127
+ ### Training Set Metrics
128
+ | Training set | Min | Median | Max |
129
+ |:-------------|:----|:--------|:----|
130
+ | Word count | 8 | 15.1667 | 27 |
131
+
132
+ | Label | Training Sample Count |
133
+ |:------|:----------------------|
134
+ | 0 | 10 |
135
+ | 1 | 10 |
136
+ | 2 | 10 |
137
+ | 3 | 10 |
138
+ | 4 | 10 |
139
+ | 5 | 10 |
140
+
141
+ ### Training Hyperparameters
142
+ - batch_size: (16, 16)
143
+ - num_epochs: (3, 3)
144
+ - max_steps: -1
145
+ - sampling_strategy: oversampling
146
+ - num_iterations: 20
147
+ - body_learning_rate: (2e-05, 2e-05)
148
+ - head_learning_rate: 2e-05
149
+ - loss: CosineSimilarityLoss
150
+ - distance_metric: cosine_distance
151
+ - margin: 0.25
152
+ - end_to_end: False
153
+ - use_amp: False
154
+ - warmup_proportion: 0.1
155
+ - seed: 42
156
+ - eval_max_steps: -1
157
+ - load_best_model_at_end: False
158
+
159
+ ### Training Results
160
+ | Epoch | Step | Training Loss | Validation Loss |
161
+ |:------:|:----:|:-------------:|:---------------:|
162
+ | 0.0067 | 1 | 0.3577 | - |
163
+ | 0.3333 | 50 | 0.04 | - |
164
+ | 0.6667 | 100 | 0.002 | - |
165
+ | 1.0 | 150 | 0.0013 | - |
166
+ | 1.3333 | 200 | 0.0009 | - |
167
+ | 1.6667 | 250 | 0.0006 | - |
168
+ | 2.0 | 300 | 0.0006 | - |
169
+ | 2.3333 | 350 | 0.0004 | - |
170
+ | 2.6667 | 400 | 0.0006 | - |
171
+ | 3.0 | 450 | 0.0004 | - |
172
+
173
+ ### Framework Versions
174
+ - Python: 3.10.12
175
+ - SetFit: 1.0.3
176
+ - Sentence Transformers: 2.7.0
177
+ - Transformers: 4.40.1
178
+ - PyTorch: 2.2.1+cu121
179
+ - Datasets: 2.19.0
180
+ - Tokenizers: 0.19.1
181
+
182
+ ## Citation
183
+
184
+ ### BibTeX
185
+ ```bibtex
186
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
187
+ doi = {10.48550/ARXIV.2209.11055},
188
+ url = {https://arxiv.org/abs/2209.11055},
189
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
190
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
191
+ title = {Efficient Few-Shot Learning Without Prompts},
192
+ publisher = {arXiv},
193
+ year = {2022},
194
+ copyright = {Creative Commons Attribution 4.0 International}
195
+ }
196
+ ```
197
+
198
+ <!--
199
+ ## Glossary
200
+
201
+ *Clearly define terms in order to be accessible across audiences.*
202
+ -->
203
+
204
+ <!--
205
+ ## Model Card Authors
206
+
207
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
208
+ -->
209
+
210
+ <!--
211
+ ## Model Card Contact
212
+
213
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
214
+ -->
config.json ADDED
@@ -0,0 +1,28 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "intfloat/multilingual-e5-large",
3
+ "architectures": [
4
+ "XLMRobertaModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": 0,
8
+ "classifier_dropout": null,
9
+ "eos_token_id": 2,
10
+ "hidden_act": "gelu",
11
+ "hidden_dropout_prob": 0.1,
12
+ "hidden_size": 1024,
13
+ "initializer_range": 0.02,
14
+ "intermediate_size": 4096,
15
+ "layer_norm_eps": 1e-05,
16
+ "max_position_embeddings": 514,
17
+ "model_type": "xlm-roberta",
18
+ "num_attention_heads": 16,
19
+ "num_hidden_layers": 24,
20
+ "output_past": true,
21
+ "pad_token_id": 1,
22
+ "position_embedding_type": "absolute",
23
+ "torch_dtype": "float32",
24
+ "transformers_version": "4.40.1",
25
+ "type_vocab_size": 1,
26
+ "use_cache": true,
27
+ "vocab_size": 250002
28
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "2.7.0",
4
+ "transformers": "4.40.1",
5
+ "pytorch": "2.2.1+cu121"
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 sha256:610de9b989f1d49847bb7ec1fc83a179591a8ee56eec08bf127c79758170fe3c
3
+ size 2239607176
model_head.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4b09eae53d4c90f46abbee7017544211c093b09a133e60d9f48dd2542a0f9807
3
+ size 50087
modules.json ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ {
15
+ "idx": 2,
16
+ "name": "2",
17
+ "path": "2_Normalize",
18
+ "type": "sentence_transformers.models.Normalize"
19
+ }
20
+ ]
sentence_bert_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "max_seq_length": 512,
3
+ "do_lower_case": false
4
+ }
sentencepiece.bpe.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
3
+ size 5069051
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
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
3
+ size 17082987
tokenizer_config.json ADDED
@@ -0,0 +1,54 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ "3": {
28
+ "content": "<unk>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "250001": {
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
+ "eos_token": "</s>",
48
+ "mask_token": "<mask>",
49
+ "model_max_length": 512,
50
+ "pad_token": "<pad>",
51
+ "sep_token": "</s>",
52
+ "tokenizer_class": "XLMRobertaTokenizer",
53
+ "unk_token": "<unk>"
54
+ }