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Gopal2002
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setfit_zeon_3456

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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  1. 1_Pooling/config.json +7 -0
  2. README.md +256 -0
  3. config.json +31 -0
  4. config_sentence_transformers.json +7 -0
  5. config_setfit.json +4 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +20 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +7 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +57 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": true,
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+ "pooling_mode_mean_tokens": false,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false
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+ }
README.md ADDED
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+ ---
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+ library_name: setfit
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: BAAI/bge-small-en-v1.5
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+ model-index:
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+ - name: SetFit with BAAI/bge-small-en-v1.5
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 1.0
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with BAAI/bge-small-en-v1.5
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 4 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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+ | 5 | <ul><li>'<s_cord-v2><s_menu><s_nm> MCLIPLE Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie Cookie 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THOUS NICE.COMPANY SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY CHOCO SPICY 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+ | 3 | <ul><li>'<s_cord-v2><s_menu><s_nm> HNDALCO INDUSTRIES LIMITED HIRAKUD</s_nm></s_sub><sep/><s_nm> PAYMENT ORDER</s_nm></s_sub><sep/><s_nm> SAWALEWARI PHARMACENTRALS</s_nm><s_cnt> 1</s_cnt><s_price> Payto SAMALESWANGAUPUR</s_nm><s_cnt> 1</s_cnt><s_price> -</s_price><sep/><s_nm> BADYANATH CHOWK SAMBALPUR</s_nm><s_cnt> 1</s_cnt><s_price> -210%</s_price><sep/><s_nm> Emplo.SCode N.Gang HIRAKUD</s_nm></s_sub><sep/><s_nm> CaghCheuud D.I/Transfer The sun of (R.163,701.72)</s_nm><s_unitprice> 163556</s_unitprice><s_cnt> 1</s_cnt><s_price> A.P.V.N.NallullahG3</s_price><sep/><s_nm> Rupes ONE LKING SIXY THRKEWENT HUNDRED ONE AND SEYWNYYAISE ONLY</s_nm></s_sub><sep/><s_nm> Details of Pavment</s_nm><s_unitprice> of Ayment</s_nm><s_unitprice> Dellate Amount CAST OF UNAVAILABLE MEDICINCES PURGHASE FROM OUTSIDE</s_nm><s_cnt> 1</s_cnt><s_price> Ra.194,83.00</s_price><sep/><s_nm> FOR OUR EMPLOYEE FROM D.11.08: 2021</s_nm><s_unitprice> 20.08-2011</s_unitprice><s_cnt> 1</s_cnt><s_price> -</s_price><sep/><s_nm> INVOLICE NO-0121</s_nm></s_sub><sep/><s_nm> RATE & DISCOUNT 16% ON WASHUUM RETAIL PRICE</s_nm><s_unitprice> Rs.</s_nm><s_cnt> 1</s_cnt><s_price> (311.28)</s_price></s_menu><s_sub_total><s_subtotal_price> 13.000</s_subtotal_price><s_discount_price> AQ. 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V.Nchovequen D/Transfertie sum of Rs.3,500.00</s_discountprice><s_price> Rp.3,500.00</s_price><sep/><s_nm> Rupess THREE THOUSAND FIVE HUNDRED ONLY</s_nm><s_num> DATE</s_nm><s_num> DATE</s_nm><s_num> DATE</s_nm><s_num> DATE</s_nm><s_num> DAYOUN Amount</s_nm><s_num> DAYON BKPENSES ON AMBULANCE TO TAKE FOR M R. K. 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Stie.B</s_nm><s_num> Caoh V.N.I</s_nm><s_num> D.Affensietie sun of</s_nm><s_num> N144</s_num><s_unitprice> 44,073.00</s_unitprice><s_cnt> 1.00</s_unitprice><s_cnt> 1.00</s_cnt><s_price> 3035.0018</s_price><sep/><s_nm> Rupess Pory Four Thousang Seventey Three Only</s_nm><s_num> DATE</s_nm><s_num> Distus of Payment</s_nm><s_num> Amount</s_nm><s_num> Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow Allow A'</li></ul> |
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+ | 6 | <ul><li>'<s_cord-v2><s_menu><s_nm> R T,41901-04/ ( Ruppes Seven lave</s_nm><s_unitprice> and</s_unitprice><s_cnt> R</s_cnt><s_price> thousand nine hundred one</s_nm><s_unitprice> and</s_unitprice><s_cnt> 2</s_cnt><s_price> touch paisa</s_nm><sep/><s_nm> only) may Kind be teckageol tor bill</s_nm><s_price> paymade</s_nm></s_sub><sep/><s_nm> toucheds Ambang Coal tavoin</s_nm><s_unitprice> mailway with way ways Escavators at really sding & Spacifique</s_nm><sep/><s_nm> the card trom Kailuray Siding To Cruel yang</s_nm><sep/><s_nm> WBING HYA-Tipper ChocM-</s_nm><s_unitprice> 2nos CAMESE</s_nm><s_price> BOCMI -</s_nm><s_unitprice> DL:</s_nm><s_unitprice> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'</li><li>'<s_cord-v2><s_menu><s_nm> Ministro機構にも Lid</s_nm><s_unitprice> WHOUGHEDGE PICAL</s_nm><s_unitprice> WHOUGHEDGE TEA LATTE</s_nm><s_unitprice> WHOUGHEDGE TEA LATTE</s_nm><s_unitprice> WHOUGHEDGE TEA LATTE</s_nm><s_unitprice> WHOUGHEDGE TEA LATTE</s_nm><s_unitprice> WHOUGHEDGE TEATHOUSHIKO</s_nm></s_sub><sep/><s_nm> WHOUGHEDGE TEATAKE</s_nm><s_unitprice> WHOUGHEDGE SUSHIKO</s_nm><s_unitprice> WHOUGHEDGE TEATAKE</s_nm><s_unitprice> WHONG SATUCE</s_nm></s_sub><sep/><s_nm> WHOUGHEDGE TEATAKE</s_nm><s_unitprice> WHONGELDAYOG</s_nm><s_unitprice> WHOUGHEDGE TEATAKE</s_nm><s_unitprice> WHONG</s_nm><s_unitprice> WHONGEDGE TEATAKE</s_nm><s_unitprice> WHONG</s_nm><s_unitprice> WHONG SUSHIKO</s_nm><s_unitprice> WHONG SUSHIKO</s_nm><s_unitprice> WHONG SUSHIKO</s_nm><s_unitprice> WHONG SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONGHONG SUSHIKONG SUSHIKONGHONG SUSHIKONG SUSHIKONGHONG SUSHIKONG SUSHIKONGHONG SUSHIKONG SUSHIKONGHONG SUSHIKONG SUSHIKONG SUSHIKONGHONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKONG SUSHIKO SUSHIKONG SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUS'</li><li>'<s_cord-v2><s_menu><s_nm> GoPAL DHABA</s_nm><s_unitprice> Rp. Jujomura</s_nm><s_unitprice> Mob-955678252</s_unitprice><s_cnt> 1</s_cnt><s_price> 8</s_price><sep/><s_nm> Sanyash & Allowe</s_nm><s_unitprice>.0.71.8</s_unitprice><s_cnt> 1</s_cnt><s_price> Rp..0.27.818</s_price><sep/><s_nm> Sl. No.</s_nm></s_sub><sep/><s_nm> Particulars Rate</s_nm><s_unitprice> Rate</s_nm><s_cnt> 1</s_cnt><s_price> Rp.027.818</s_price></s_menu><s_total><s_total_price> 800</s_total_price><s_creditcardprice> 0</s_creditcardprice></s_total>'</li></ul> |
119
+
120
+ ## Evaluation
121
+
122
+ ### Metrics
123
+ | Label | Accuracy |
124
+ |:--------|:---------|
125
+ | **all** | 1.0 |
126
+
127
+ ## Uses
128
+
129
+ ### Direct Use for Inference
130
+
131
+ First install the SetFit library:
132
+
133
+ ```bash
134
+ pip install setfit
135
+ ```
136
+
137
+ Then you can load this model and run inference.
138
+
139
+ ```python
140
+ from setfit import SetFitModel
141
+
142
+ # Download from the 🤗 Hub
143
+ model = SetFitModel.from_pretrained("Gopal2002/setfit_zeon_3456")
144
+ # Run inference
145
+ preds = model("<s_cord-v2><s_menu><s_nm> RT @kanaka</s_total>")
146
+ ```
147
+
148
+ <!--
149
+ ### Downstream Use
150
+
151
+ *List how someone could finetune this model on their own dataset.*
152
+ -->
153
+
154
+ <!--
155
+ ### Out-of-Scope Use
156
+
157
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
158
+ -->
159
+
160
+ <!--
161
+ ## Bias, Risks and Limitations
162
+
163
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
164
+ -->
165
+
166
+ <!--
167
+ ### Recommendations
168
+
169
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
170
+ -->
171
+
172
+ ## Training Details
173
+
174
+ ### Training Set Metrics
175
+ | Training set | Min | Median | Max |
176
+ |:-------------|:----|:---------|:----|
177
+ | Word count | 2 | 123.7582 | 762 |
178
+
179
+ | Label | Training Sample Count |
180
+ |:------|:----------------------|
181
+ | 3 | 16 |
182
+ | 4 | 24 |
183
+ | 5 | 22 |
184
+ | 6 | 29 |
185
+
186
+ ### Training Hyperparameters
187
+ - batch_size: (32, 32)
188
+ - num_epochs: (2, 2)
189
+ - max_steps: -1
190
+ - sampling_strategy: oversampling
191
+ - body_learning_rate: (2e-05, 1e-05)
192
+ - head_learning_rate: 0.01
193
+ - loss: CosineSimilarityLoss
194
+ - distance_metric: cosine_distance
195
+ - margin: 0.25
196
+ - end_to_end: False
197
+ - use_amp: False
198
+ - warmup_proportion: 0.1
199
+ - seed: 42
200
+ - eval_max_steps: -1
201
+ - load_best_model_at_end: False
202
+
203
+ ### Training Results
204
+ | Epoch | Step | Training Loss | Validation Loss |
205
+ |:------:|:----:|:-------------:|:---------------:|
206
+ | 0.0052 | 1 | 0.2983 | - |
207
+ | 0.2604 | 50 | 0.1069 | - |
208
+ | 0.5208 | 100 | 0.0221 | - |
209
+ | 0.7812 | 150 | 0.0063 | - |
210
+ | 1.0417 | 200 | 0.0039 | - |
211
+ | 1.3021 | 250 | 0.0029 | - |
212
+ | 1.5625 | 300 | 0.003 | - |
213
+ | 1.8229 | 350 | 0.0027 | - |
214
+
215
+ ### Framework Versions
216
+ - Python: 3.10.12
217
+ - SetFit: 1.0.2
218
+ - Sentence Transformers: 2.2.2
219
+ - Transformers: 4.35.2
220
+ - PyTorch: 2.1.0+cu121
221
+ - Datasets: 2.16.1
222
+ - Tokenizers: 0.15.0
223
+
224
+ ## Citation
225
+
226
+ ### BibTeX
227
+ ```bibtex
228
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
229
+ doi = {10.48550/ARXIV.2209.11055},
230
+ url = {https://arxiv.org/abs/2209.11055},
231
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
232
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
233
+ title = {Efficient Few-Shot Learning Without Prompts},
234
+ publisher = {arXiv},
235
+ year = {2022},
236
+ copyright = {Creative Commons Attribution 4.0 International}
237
+ }
238
+ ```
239
+
240
+ <!--
241
+ ## Glossary
242
+
243
+ *Clearly define terms in order to be accessible across audiences.*
244
+ -->
245
+
246
+ <!--
247
+ ## Model Card Authors
248
+
249
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
250
+ -->
251
+
252
+ <!--
253
+ ## Model Card Contact
254
+
255
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
256
+ -->
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