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mohamednihal
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Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
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mohamednihal commited on
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Add SetFit model

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  1. 1_Pooling/config.json +10 -0
  2. README.md +396 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +9 -0
  5. config_setfit.json +15 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +51 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +59 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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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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+ widget:
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+ - text: 'Dear [Recipient],
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+ is required. Should you have any questions or require further assistance, please
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+ project goals.
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+ - text: 'Hello [Recipient''s Name],
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+
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+ As a valued member of our community, we''re delighted to offer you an exclusive
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+ discount! Enjoy [Discount percentage]% off your next purchase when you use code
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+ [Promo code] at checkout.
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+ Explore our latest arrivals and treat yourself to something special. But hurry
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+ - this offer expires soon!
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+ Shop now at [Link to Website] and enjoy savings on us.
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+
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+ Best regards,
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+
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+ [Your Name]
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+
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+ [Your Position/Department]
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+ [Your Contact Information]'
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: sentence-transformers/paraphrase-mpnet-base-v2
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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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 [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.
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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:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
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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:** 10 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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+ | Security Alerts | <ul><li>"Dear [Recipient],\n\nWe are writing to inform you about an urgent security alert that requires your immediate attention.\n\n[Describe the security issue or threat briefly and clearly.]\n\nTo ensure the safety and integrity of your account/data, we highly recommend taking the following actions immediately:\n\nChange your password: Please reset your password using a strong and unique combination of characters.\nEnable two-factor authentication (2FA): Add an extra layer of security to your account by enabling 2FA if you haven't already done so.\nReview recent account activity: Check for any suspicious or unauthorized activities and report them to our support team immediately.\nIf you have any concerns or require assistance with securing your account, please contact our security team at [security email or phone number].\n\nYour prompt attention to this matter is crucial in safeguarding your information and preventing potential security breaches.\n\nThank you for your cooperation and understanding.\n\nBest regards,\n\n[Your Name]\n[Your Position/Department (if applicable)]\n[Your Company Name]\n[Your Contact Information]"</li><li>"Dear [Recipient],\n\nWe are writing to inform you about a potential phishing attempt that has been detected targeting users of our platform.\n\n[Describe the phishing attempt briefly, including any relevant details or indicators of the phishing email.]\n\nTo safeguard your account and personal information, please remember the following precautions:\n\nDo not click on any links or download attachments from suspicious emails.\nVerify the sender's email address and look for any signs of spoofing or impersonation.\nReport any suspicious emails to our security team immediately for further investigation.\nIf you have inadvertently clicked on any links or provided any sensitive information, please change your account password immediately and notify our support team for assistance.\n\nThank you for your attention to this matter and for helping us maintain a secure environment for all our users.\n\nBest regards,\n\n[Your Name]\n[Your Position/Department (if applicable)]\n[Your Company Name]\n[Your Contact Information]"</li><li>'Dear [Recipient],\n\nWe regret to inform you that your account has been temporarily locked due to multiple failed login attempts.\n\n[Explain the reason for the account lockout, such as exceeding the maximum allowed login attempts or suspicious login activity.]\n\nTo regain access to your account, please follow these steps:\n\nVisit our login page and click on the "Forgot Password" link.\nFollow the instructions to reset your password securely.\nAfter resetting your password, you will be able to log in to your account as usual.\nIf you believe this lockout was initiated in error or have any concerns about the security of your account, please contact our support team immediately for assistance.\n\nThank you for your cooperation and understanding.\n\nBest regards,\n\n[Your Name]\n[Your Position/Department (if applicable)]\n[Your Company Name]\n[Your Contact Information]'</li></ul> |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("mohamednihal/no_reply")
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+ # Run inference
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+ preds = model("Hello,
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+
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+ Thank you for your email. I am currently attending a conference and may have limited access to email until [Date of Return]. I will respond to your message as soon as possible upon my return. For urgent matters, please contact [Alternative Contact Information].
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+
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+ Best regards,
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+ [Your Name]")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
216
+
217
+ <!--
218
+ ## Bias, Risks and Limitations
219
+
220
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
221
+ -->
222
+
223
+ <!--
224
+ ### Recommendations
225
+
226
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
227
+ -->
228
+
229
+ ## Training Details
230
+
231
+ ### Training Set Metrics
232
+ | Training set | Min | Median | Max |
233
+ |:-------------|:----|:-------|:----|
234
+ | Word count | 44 | 85.24 | 170 |
235
+
236
+ | Label | Training Sample Count |
237
+ |:----------------------------------------------------------------|:----------------------|
238
+ | Automatic subscription confirmation emails | 5 |
239
+ | Do-not-reply or no-reply emails (explicitly mentioned in email) | 5 |
240
+ | Email Delivery failure notifications | 5 |
241
+ | Out-of- office replies | 5 |
242
+ | Password reset emails | 5 |
243
+ | Promotional emails | 5 |
244
+ | Read receipts or delivery confirmation | 5 |
245
+ | Reminders | 5 |
246
+ | Security Alerts | 5 |
247
+ | System Alerts | 5 |
248
+
249
+ ### Training Hyperparameters
250
+ - batch_size: (2, 2)
251
+ - num_epochs: (1, 1)
252
+ - max_steps: -1
253
+ - sampling_strategy: oversampling
254
+ - num_iterations: 20
255
+ - body_learning_rate: (2.887681170264626e-05, 2.887681170264626e-05)
256
+ - head_learning_rate: 2.887681170264626e-05
257
+ - loss: CosineSimilarityLoss
258
+ - distance_metric: cosine_distance
259
+ - margin: 0.25
260
+ - end_to_end: False
261
+ - use_amp: False
262
+ - warmup_proportion: 0.1
263
+ - seed: 42
264
+ - eval_max_steps: -1
265
+ - load_best_model_at_end: False
266
+
267
+ ### Training Results
268
+ | Epoch | Step | Training Loss | Validation Loss |
269
+ |:-----:|:----:|:-------------:|:---------------:|
270
+ | 0.001 | 1 | 0.1811 | - |
271
+ | 0.05 | 50 | 0.3964 | - |
272
+ | 0.1 | 100 | 0.0705 | - |
273
+ | 0.15 | 150 | 0.0115 | - |
274
+ | 0.2 | 200 | 0.0477 | - |
275
+ | 0.25 | 250 | 0.0022 | - |
276
+ | 0.3 | 300 | 0.0044 | - |
277
+ | 0.35 | 350 | 0.0017 | - |
278
+ | 0.4 | 400 | 0.001 | - |
279
+ | 0.45 | 450 | 0.0001 | - |
280
+ | 0.5 | 500 | 0.0006 | - |
281
+ | 0.55 | 550 | 0.0008 | - |
282
+ | 0.6 | 600 | 0.0003 | - |
283
+ | 0.65 | 650 | 0.0006 | - |
284
+ | 0.7 | 700 | 0.0003 | - |
285
+ | 0.75 | 750 | 0.0017 | - |
286
+ | 0.8 | 800 | 0.0001 | - |
287
+ | 0.85 | 850 | 0.0002 | - |
288
+ | 0.9 | 900 | 0.0 | - |
289
+ | 0.95 | 950 | 0.0002 | - |
290
+ | 1.0 | 1000 | 0.0002 | - |
291
+ | 0.001 | 1 | 0.0001 | - |
292
+ | 0.05 | 50 | 0.0002 | - |
293
+ | 0.1 | 100 | 0.0014 | - |
294
+ | 0.15 | 150 | 0.008 | - |
295
+ | 0.2 | 200 | 0.0017 | - |
296
+ | 0.25 | 250 | 0.0018 | - |
297
+ | 0.3 | 300 | 0.0187 | - |
298
+ | 0.35 | 350 | 0.0021 | - |
299
+ | 0.4 | 400 | 0.0001 | - |
300
+ | 0.45 | 450 | 0.0 | - |
301
+ | 0.5 | 500 | 0.0003 | - |
302
+ | 0.55 | 550 | 0.0001 | - |
303
+ | 0.6 | 600 | 0.0 | - |
304
+ | 0.65 | 650 | 0.0002 | - |
305
+ | 0.7 | 700 | 0.0 | - |
306
+ | 0.75 | 750 | 0.0003 | - |
307
+ | 0.8 | 800 | 0.0 | - |
308
+ | 0.85 | 850 | 0.0 | - |
309
+ | 0.9 | 900 | 0.0001 | - |
310
+ | 0.95 | 950 | 0.0001 | - |
311
+ | 1.0 | 1000 | 0.0 | - |
312
+ | 0.001 | 1 | 0.0 | - |
313
+ | 0.05 | 50 | 0.0001 | - |
314
+ | 0.1 | 100 | 0.0018 | - |
315
+ | 0.15 | 150 | 0.0001 | - |
316
+ | 0.2 | 200 | 0.0042 | - |
317
+ | 0.25 | 250 | 0.0009 | - |
318
+ | 0.3 | 300 | 0.0001 | - |
319
+ | 0.35 | 350 | 0.0018 | - |
320
+ | 0.4 | 400 | 0.0002 | - |
321
+ | 0.45 | 450 | 0.0001 | - |
322
+ | 0.5 | 500 | 0.0 | - |
323
+ | 0.55 | 550 | 0.0001 | - |
324
+ | 0.6 | 600 | 0.0 | - |
325
+ | 0.65 | 650 | 0.0 | - |
326
+ | 0.7 | 700 | 0.0 | - |
327
+ | 0.75 | 750 | 0.0 | - |
328
+ | 0.8 | 800 | 0.0 | - |
329
+ | 0.85 | 850 | 0.0 | - |
330
+ | 0.9 | 900 | 0.0 | - |
331
+ | 0.95 | 950 | 0.0 | - |
332
+ | 1.0 | 1000 | 0.0 | - |
333
+ | 0.001 | 1 | 0.0 | - |
334
+ | 0.05 | 50 | 0.0 | - |
335
+ | 0.1 | 100 | 0.0005 | - |
336
+ | 0.15 | 150 | 0.0025 | - |
337
+ | 0.2 | 200 | 0.0 | - |
338
+ | 0.25 | 250 | 0.0002 | - |
339
+ | 0.3 | 300 | 0.0 | - |
340
+ | 0.35 | 350 | 0.0003 | - |
341
+ | 0.4 | 400 | 0.0001 | - |
342
+ | 0.45 | 450 | 0.0 | - |
343
+ | 0.5 | 500 | 0.0 | - |
344
+ | 0.55 | 550 | 0.0 | - |
345
+ | 0.6 | 600 | 0.0 | - |
346
+ | 0.65 | 650 | 0.0 | - |
347
+ | 0.7 | 700 | 0.0 | - |
348
+ | 0.75 | 750 | 0.0 | - |
349
+ | 0.8 | 800 | 0.0 | - |
350
+ | 0.85 | 850 | 0.0 | - |
351
+ | 0.9 | 900 | 0.0 | - |
352
+ | 0.95 | 950 | 0.0 | - |
353
+ | 1.0 | 1000 | 0.0 | - |
354
+
355
+ ### Framework Versions
356
+ - Python: 3.10.12
357
+ - SetFit: 1.0.3
358
+ - Sentence Transformers: 2.4.0
359
+ - Transformers: 4.37.2
360
+ - PyTorch: 2.1.0+cu121
361
+ - Datasets: 2.17.1
362
+ - Tokenizers: 0.15.2
363
+
364
+ ## Citation
365
+
366
+ ### BibTeX
367
+ ```bibtex
368
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
369
+ doi = {10.48550/ARXIV.2209.11055},
370
+ url = {https://arxiv.org/abs/2209.11055},
371
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
372
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
373
+ title = {Efficient Few-Shot Learning Without Prompts},
374
+ publisher = {arXiv},
375
+ year = {2022},
376
+ copyright = {Creative Commons Attribution 4.0 International}
377
+ }
378
+ ```
379
+
380
+ <!--
381
+ ## Glossary
382
+
383
+ *Clearly define terms in order to be accessible across audiences.*
384
+ -->
385
+
386
+ <!--
387
+ ## Model Card Authors
388
+
389
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
390
+ -->
391
+
392
+ <!--
393
+ ## Model Card Contact
394
+
395
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
396
+ -->
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