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README.md
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- text-classification
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- generated_from_setfit_trainer
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metrics:
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widget:
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as much science in that as the crap she spouts
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- text: "Can she skip school by herself and sit infront of parliament? \r\n Fake emotions
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\ and just a good actor."
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- text: my dad had huge ones..so they may be real..
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pipeline_tag: text-classification
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inference:
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base_model: sentence-transformers/paraphrase-mpnet-base-v2
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model-index:
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- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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split: test
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metrics:
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value: 0.
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name: Metric
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---
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#
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2. Training a classification head with features from the fine-tuned Sentence Transformer.
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## Model Details
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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:** 13 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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### Model Sources
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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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### Model Labels
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| Label | Examples |
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|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| 5 | <ul><li>'please show us the evidence I asked for'</li><li>'Please follow https://youtu.be/WpTCt-S-qLM ??'</li><li>'Answer the question. The first is illegal in NY, the second is legal.'</li></ul> |
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| 7 | <ul><li>'This guy sounds like he needs to clear his throat'</li><li>'very goiot'</li><li>'Semen ???? what kind of name it is????'</li></ul> |
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| 9 | <ul><li>'Yeah I can do that.'</li><li>"I can totally accept that the government fund green energy, it's for the best."</li><li>'Yes, he is right ! My Dr. did exactly what he is saying. He started antibiotics then 5 days started the steroids. Hopefully other dr will do the same.'</li></ul> |
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| 0 | <ul><li>'If only most senates in the US can see how climate change can not only effect our planets environment, it also our economys like theirs.'</li><li>'1st Comment!'</li><li>'People moving interstate will reduce unemployment in SA.'</li></ul> |
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| 2 | <ul><li>'ok boomer'</li><li>'I can see where ur coming from she did invite this cousin to live there which mind you is likely an act of kindness, and it is ur private space. But tbh you are also being extremely petty, this man is on the couch, he has early morning shifts, using ur bathroom would not disturb a single person, while using ur roommates means this man has to be very uncomfortable walk through a sleeping persons room every morning and sneak back out again. Likely waking up the cousin in the process. Thats why I believe its an Everyone Sucks here cause theyre both asshole like moves.'</li><li>'suggesting the vaccine to women who are pregnant, when it can cause for many women earlier heavier longer periods, means it triggers a period, we know women in our personal lives that have experienced that along with their period coming twice that month, and we know to avoid foods that can trigger a period because that could potentially cause a miscarriage, so why suggest it?? this is how the whole world will lose confidence in science and medicine because they can down right lie to our face claiming they understand more then you meanwhile propagating the agenda of pharmaceutical companies. absaloutly disgusting and shame on you for betraying our trust.'</li></ul> |
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| 3 | <ul><li>'Nothing is hotter than Shawn, not even the sun mate??'</li><li>'Incorrect. Geologists have not claimed that warming "historically has always been accompanied by a mass extinction event".'</li><li>'No, it doesnt change anything in cats or in humans. Vaccines dont cause autism.'</li></ul> |
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| 6 | <ul><li>'So youre taking a government course?'</li><li>'Is this journalist on work experience ?'</li><li>'Who is here after the movie'</li></ul> |
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| 4 | <ul><li>'Forward looking required now, which the leaders are doing and doing their best, sleep deprived and a world of responsibility on them. Thanks to all'</li><li>"Oh, great, you could do that? That'd help me out really."</li><li>"Couldn't be more proud and happy that these heroes are finally taking a stand to the horrible ways the current government is pushing the country"</li></ul> |
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| 1 | <ul><li>"Maybe STOP paying commanders who have NEVER even picked up a hose and give the power back to the brigades CAPTAIN'S. And things will start to get better, from someone who is actually doing something. Snowy mountains Australia."</li><li>"@Keith Bawden You have an education in science? Me too! Did you study in any field relevant to the topic? I am currently doing a PhD in biogeochemistry, and my research group is involved in climate science. I can tell you the VAST majority of scientists in fields directly related to or peripheral to climate change accept that it is indeed a real phenomenon, and it is caused by humans. The exceptions you can name are exactly that, exceptions. I'll grant you, Zarkhova may be right about a coming grand solar minimum, but even if so, all it would do is slightly slow temperature increase. There would be no mini ice age (Fuelner and Rahmstorf, 2010). The question is, in 2-3 years, when a 'mini ice age' does not occur, will you change your mind, or find some other reason to deny?"</li><li>"You should have gotten herd immunity in Changi, considering 95% efficacy of Pfizer and 80% or more are vaccinated.\r\nIt's either the efficacy is faked or the vaccine is useless against the indian variant."</li></ul> |
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| 10 | <ul><li>'No Im not trolling.'</li><li>"I'm not being hurtful. I'm being honest. You need to vaccinate your cat"</li><li>'I said no such thing. The OP asked about equipment, not lifting. There are probably dozens, even hundreds, or submissions on here about lifting technique he can research.'</li></ul> |
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| 8 | <ul><li>'Maybe you could try drinking more tea instead of coffee.'</li><li>'I think you should definitely take steroids. every single steroid you can find, take it every single day.'</li><li>'The government should buy more masks for the public.'</li></ul> |
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| 11 | <ul><li>'@Constantine Nikitin.I did it.'</li><li>"Ok, you're right, it was my fault, I shouldn't have said that."</li><li>'Sure, I was wrong.'</li></ul> |
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| 12 | <ul><li>"Things got out of hand, I'm sorry."</li><li>'Oh no, I did not mean it that way, it was completely misunderstood what I was saying. Didnt mean to offend you, sorry!'</li><li>'Sorry.'</li></ul> |
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## Evaluation
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### Metrics
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| Label | Metric |
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| **all** | 0.5465 |
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## Uses
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### Direct Use for Inference
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First install the SetFit library:
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```bash
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pip install setfit
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```
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Then you can load this model and run inference.
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```python
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from setfit import SetFitModel
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("CrisisNarratives/setfit-13classes-single_label")
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# Run inference
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preds = model("my dad had huge ones..so they may be real..")
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```
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### Downstream Use
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*List how someone could finetune this model on their own dataset.*
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### Out-of-Scope Use
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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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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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| Training set | Min | Median | Max |
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| Word count | 1 | 25.8891 | 1681 |
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- body_learning_rate: (1.698e-05, 1.698e-05)
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- head_learning_rate: 1.698e-05
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- loss: CosineSimilarityLoss
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- seed: 39
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| 2.3475 | 6350 | 0.0002 | - |
|
| 310 |
-
| 2.3660 | 6400 | 0.0002 | - |
|
| 311 |
-
| 2.3845 | 6450 | 0.0001 | - |
|
| 312 |
-
| 2.4030 | 6500 | 0.0001 | - |
|
| 313 |
-
| 2.4214 | 6550 | 0.0001 | - |
|
| 314 |
-
| 2.4399 | 6600 | 0.0001 | - |
|
| 315 |
-
| 2.4584 | 6650 | 0.0001 | - |
|
| 316 |
-
| 2.4769 | 6700 | 0.0001 | - |
|
| 317 |
-
| 2.4954 | 6750 | 0.0001 | - |
|
| 318 |
-
| 2.5139 | 6800 | 0.0001 | - |
|
| 319 |
-
| 2.5323 | 6850 | 0.0002 | - |
|
| 320 |
-
| 2.5508 | 6900 | 0.0001 | - |
|
| 321 |
-
| 2.5693 | 6950 | 0.0003 | - |
|
| 322 |
-
| 2.5878 | 7000 | 0.0001 | - |
|
| 323 |
-
| 2.6063 | 7050 | 0.0001 | - |
|
| 324 |
-
| 2.6248 | 7100 | 0.0001 | - |
|
| 325 |
-
| 2.6433 | 7150 | 0.0009 | - |
|
| 326 |
-
| 2.6617 | 7200 | 0.0004 | - |
|
| 327 |
-
| 2.6802 | 7250 | 0.0001 | - |
|
| 328 |
-
| 2.6987 | 7300 | 0.0 | - |
|
| 329 |
-
| 2.7172 | 7350 | 0.0002 | - |
|
| 330 |
-
| 2.7357 | 7400 | 0.0001 | - |
|
| 331 |
-
| 2.7542 | 7450 | 0.0001 | - |
|
| 332 |
-
| 2.7726 | 7500 | 0.0 | - |
|
| 333 |
-
| 2.7911 | 7550 | 0.0001 | - |
|
| 334 |
-
| 2.8096 | 7600 | 0.0001 | - |
|
| 335 |
-
| 2.8281 | 7650 | 0.0001 | - |
|
| 336 |
-
| 2.8466 | 7700 | 0.0001 | - |
|
| 337 |
-
| 2.8651 | 7750 | 0.0001 | - |
|
| 338 |
-
| 2.8835 | 7800 | 0.0001 | - |
|
| 339 |
-
| 2.9020 | 7850 | 0.0001 | - |
|
| 340 |
-
| 2.9205 | 7900 | 0.0002 | - |
|
| 341 |
-
| 2.9390 | 7950 | 0.0002 | - |
|
| 342 |
-
| 2.9575 | 8000 | 0.0001 | - |
|
| 343 |
-
| 2.9760 | 8050 | 0.0001 | - |
|
| 344 |
-
| 2.9945 | 8100 | 0.0001 | - |
|
| 345 |
-
| 0.0003 | 1 | 0.0002 | - |
|
| 346 |
-
| 0.0168 | 50 | 0.0001 | - |
|
| 347 |
-
| 0.0336 | 100 | 0.0002 | - |
|
| 348 |
-
| 0.0504 | 150 | 0.0001 | - |
|
| 349 |
-
| 0.0672 | 200 | 0.0001 | - |
|
| 350 |
-
| 0.0840 | 250 | 0.0 | - |
|
| 351 |
-
| 0.1008 | 300 | 0.0001 | - |
|
| 352 |
-
| 0.1176 | 350 | 0.0001 | - |
|
| 353 |
-
| 0.1344 | 400 | 0.0001 | - |
|
| 354 |
-
| 0.1512 | 450 | 0.0004 | - |
|
| 355 |
-
| 0.1680 | 500 | 0.0001 | - |
|
| 356 |
-
| 0.1848 | 550 | 0.0003 | - |
|
| 357 |
-
| 0.2016 | 600 | 0.0003 | - |
|
| 358 |
-
| 0.2184 | 650 | 0.0007 | - |
|
| 359 |
-
| 0.2352 | 700 | 0.0005 | - |
|
| 360 |
-
| 0.2520 | 750 | 0.0 | - |
|
| 361 |
-
| 0.2688 | 800 | 0.0002 | - |
|
| 362 |
-
| 0.2856 | 850 | 0.0002 | - |
|
| 363 |
-
| 0.3024 | 900 | 0.0002 | - |
|
| 364 |
-
| 0.3192 | 950 | 0.0001 | - |
|
| 365 |
-
| 0.3360 | 1000 | 0.0002 | - |
|
| 366 |
-
| 0.3528 | 1050 | 0.0007 | - |
|
| 367 |
-
| 0.3696 | 1100 | 0.0001 | - |
|
| 368 |
-
| 0.3864 | 1150 | 0.0004 | - |
|
| 369 |
-
| 0.4032 | 1200 | 0.0002 | - |
|
| 370 |
-
| 0.4200 | 1250 | 0.0004 | - |
|
| 371 |
-
| 0.4368 | 1300 | 0.0004 | - |
|
| 372 |
-
| 0.4536 | 1350 | 0.0037 | - |
|
| 373 |
-
| 0.4704 | 1400 | 0.0406 | - |
|
| 374 |
-
| 0.4872 | 1450 | 0.0003 | - |
|
| 375 |
-
| 0.5040 | 1500 | 0.0001 | - |
|
| 376 |
-
| 0.5208 | 1550 | 0.0003 | - |
|
| 377 |
-
| 0.5376 | 1600 | 0.0002 | - |
|
| 378 |
-
| 0.5544 | 1650 | 0.0001 | - |
|
| 379 |
-
| 0.5712 | 1700 | 0.0002 | - |
|
| 380 |
-
| 0.5880 | 1750 | 0.0002 | - |
|
| 381 |
-
| 0.6048 | 1800 | 0.0001 | - |
|
| 382 |
-
| 0.6216 | 1850 | 0.0 | - |
|
| 383 |
-
| 0.6384 | 1900 | 0.0001 | - |
|
| 384 |
-
| 0.6552 | 1950 | 0.0003 | - |
|
| 385 |
-
| 0.6720 | 2000 | 0.0 | - |
|
| 386 |
-
| 0.6888 | 2050 | 0.0001 | - |
|
| 387 |
-
| 0.7056 | 2100 | 0.0003 | - |
|
| 388 |
-
| 0.7224 | 2150 | 0.0 | - |
|
| 389 |
-
| 0.7392 | 2200 | 0.1019 | - |
|
| 390 |
-
| 0.7560 | 2250 | 0.0001 | - |
|
| 391 |
-
| 0.7728 | 2300 | 0.0001 | - |
|
| 392 |
-
| 0.7897 | 2350 | 0.0001 | - |
|
| 393 |
-
| 0.8065 | 2400 | 0.0 | - |
|
| 394 |
-
| 0.8233 | 2450 | 0.0 | - |
|
| 395 |
-
| 0.8401 | 2500 | 0.0002 | - |
|
| 396 |
-
| 0.8569 | 2550 | 0.0001 | - |
|
| 397 |
-
| 0.8737 | 2600 | 0.0001 | - |
|
| 398 |
-
| 0.8905 | 2650 | 0.0001 | - |
|
| 399 |
-
| 0.9073 | 2700 | 0.0001 | - |
|
| 400 |
-
| 0.9241 | 2750 | 0.0001 | - |
|
| 401 |
-
| 0.9409 | 2800 | 0.0002 | - |
|
| 402 |
-
| 0.9577 | 2850 | 0.0 | - |
|
| 403 |
-
| 0.9745 | 2900 | 0.0001 | - |
|
| 404 |
-
| 0.9913 | 2950 | 0.0001 | - |
|
| 405 |
-
| 1.0081 | 3000 | 0.0001 | - |
|
| 406 |
-
| 1.0249 | 3050 | 0.0 | - |
|
| 407 |
-
| 1.0417 | 3100 | 0.0001 | - |
|
| 408 |
-
| 1.0585 | 3150 | 0.0001 | - |
|
| 409 |
-
| 1.0753 | 3200 | 0.0001 | - |
|
| 410 |
-
| 1.0921 | 3250 | 0.0 | - |
|
| 411 |
-
| 1.1089 | 3300 | 0.0001 | - |
|
| 412 |
-
| 1.1257 | 3350 | 0.0001 | - |
|
| 413 |
-
| 1.1425 | 3400 | 0.0001 | - |
|
| 414 |
-
| 1.1593 | 3450 | 0.0001 | - |
|
| 415 |
-
| 1.1761 | 3500 | 0.0001 | - |
|
| 416 |
-
| 1.1929 | 3550 | 0.0 | - |
|
| 417 |
-
| 1.2097 | 3600 | 0.0001 | - |
|
| 418 |
-
| 1.2265 | 3650 | 0.0 | - |
|
| 419 |
-
| 1.2433 | 3700 | 0.0001 | - |
|
| 420 |
-
| 1.2601 | 3750 | 0.0001 | - |
|
| 421 |
-
| 1.2769 | 3800 | 0.0 | - |
|
| 422 |
-
| 1.2937 | 3850 | 0.0001 | - |
|
| 423 |
-
| 1.3105 | 3900 | 0.0 | - |
|
| 424 |
-
| 1.3273 | 3950 | 0.0001 | - |
|
| 425 |
-
| 1.3441 | 4000 | 0.0002 | - |
|
| 426 |
-
| 1.3609 | 4050 | 0.0001 | - |
|
| 427 |
-
| 1.3777 | 4100 | 0.0001 | - |
|
| 428 |
-
| 1.3945 | 4150 | 0.0001 | - |
|
| 429 |
-
| 1.4113 | 4200 | 0.0 | - |
|
| 430 |
-
| 1.4281 | 4250 | 0.0001 | - |
|
| 431 |
-
| 1.4449 | 4300 | 0.0 | - |
|
| 432 |
-
| 1.4617 | 4350 | 0.0001 | - |
|
| 433 |
-
| 1.4785 | 4400 | 0.0001 | - |
|
| 434 |
-
| 1.4953 | 4450 | 0.0001 | - |
|
| 435 |
-
| 1.5121 | 4500 | 0.0001 | - |
|
| 436 |
-
| 1.5289 | 4550 | 0.0001 | - |
|
| 437 |
-
| 1.5457 | 4600 | 0.0 | - |
|
| 438 |
-
| 1.5625 | 4650 | 0.0001 | - |
|
| 439 |
-
| 1.5793 | 4700 | 0.0001 | - |
|
| 440 |
-
| 1.5961 | 4750 | 0.0001 | - |
|
| 441 |
-
| 1.6129 | 4800 | 0.0002 | - |
|
| 442 |
-
| 1.6297 | 4850 | 0.0 | - |
|
| 443 |
-
| 1.6465 | 4900 | 0.0002 | - |
|
| 444 |
-
| 1.6633 | 4950 | 0.0 | - |
|
| 445 |
-
| 1.6801 | 5000 | 0.0 | - |
|
| 446 |
-
| 1.6969 | 5050 | 0.0001 | - |
|
| 447 |
-
| 1.7137 | 5100 | 0.0001 | - |
|
| 448 |
-
| 1.7305 | 5150 | 0.0 | - |
|
| 449 |
-
| 1.7473 | 5200 | 0.0 | - |
|
| 450 |
-
| 1.7641 | 5250 | 0.0001 | - |
|
| 451 |
-
| 1.7809 | 5300 | 0.0001 | - |
|
| 452 |
-
| 1.7977 | 5350 | 0.0 | - |
|
| 453 |
-
| 1.8145 | 5400 | 0.0003 | - |
|
| 454 |
-
| 1.8313 | 5450 | 0.0 | - |
|
| 455 |
-
| 1.8481 | 5500 | 0.0001 | - |
|
| 456 |
-
| 1.8649 | 5550 | 0.0001 | - |
|
| 457 |
-
| 1.8817 | 5600 | 0.0001 | - |
|
| 458 |
-
| 1.8985 | 5650 | 0.0001 | - |
|
| 459 |
-
| 1.9153 | 5700 | 0.158 | - |
|
| 460 |
-
| 1.9321 | 5750 | 0.0012 | - |
|
| 461 |
-
| 1.9489 | 5800 | 0.0424 | - |
|
| 462 |
-
| 1.9657 | 5850 | 0.0011 | - |
|
| 463 |
-
| 1.9825 | 5900 | 0.0002 | - |
|
| 464 |
-
| 1.9993 | 5950 | 0.1197 | - |
|
| 465 |
-
| 2.0161 | 6000 | 0.0001 | - |
|
| 466 |
-
| 2.0329 | 6050 | 0.2476 | - |
|
| 467 |
-
| 2.0497 | 6100 | 0.0029 | - |
|
| 468 |
-
| 2.0665 | 6150 | 0.0 | - |
|
| 469 |
-
| 2.0833 | 6200 | 0.0 | - |
|
| 470 |
-
| 2.1001 | 6250 | 0.0 | - |
|
| 471 |
-
| 2.1169 | 6300 | 0.0001 | - |
|
| 472 |
-
| 2.1337 | 6350 | 0.1151 | - |
|
| 473 |
-
| 2.1505 | 6400 | 0.0001 | - |
|
| 474 |
-
| 2.1673 | 6450 | 0.0001 | - |
|
| 475 |
-
| 2.1841 | 6500 | 0.0003 | - |
|
| 476 |
-
| 2.2009 | 6550 | 0.1204 | - |
|
| 477 |
-
| 2.2177 | 6600 | 0.0001 | - |
|
| 478 |
-
| 2.2345 | 6650 | 0.0 | - |
|
| 479 |
-
| 2.2513 | 6700 | 0.0016 | - |
|
| 480 |
-
| 2.2681 | 6750 | 0.0001 | - |
|
| 481 |
-
| 2.2849 | 6800 | 0.0008 | - |
|
| 482 |
-
| 2.3017 | 6850 | 0.0001 | - |
|
| 483 |
-
| 2.3185 | 6900 | 0.0 | - |
|
| 484 |
-
| 2.3353 | 6950 | 0.0 | - |
|
| 485 |
-
| 2.3522 | 7000 | 0.0 | - |
|
| 486 |
-
| 2.3690 | 7050 | 0.0003 | - |
|
| 487 |
-
| 2.3858 | 7100 | 0.0 | - |
|
| 488 |
-
| 2.4026 | 7150 | 0.0 | - |
|
| 489 |
-
| 2.4194 | 7200 | 0.0001 | - |
|
| 490 |
-
| 2.4362 | 7250 | 0.0 | - |
|
| 491 |
-
| 2.4530 | 7300 | 0.0001 | - |
|
| 492 |
-
| 2.4698 | 7350 | 0.0001 | - |
|
| 493 |
-
| 2.4866 | 7400 | 0.0001 | - |
|
| 494 |
-
| 2.5034 | 7450 | 0.0 | - |
|
| 495 |
-
| 2.5202 | 7500 | 0.0001 | - |
|
| 496 |
-
| 2.5370 | 7550 | 0.0001 | - |
|
| 497 |
-
| 2.5538 | 7600 | 0.0 | - |
|
| 498 |
-
| 2.5706 | 7650 | 0.0 | - |
|
| 499 |
-
| 2.5874 | 7700 | 0.0 | - |
|
| 500 |
-
| 2.6042 | 7750 | 0.0002 | - |
|
| 501 |
-
| 2.6210 | 7800 | 0.0001 | - |
|
| 502 |
-
| 2.6378 | 7850 | 0.0001 | - |
|
| 503 |
-
| 2.6546 | 7900 | 0.0 | - |
|
| 504 |
-
| 2.6714 | 7950 | 0.0001 | - |
|
| 505 |
-
| 2.6882 | 8000 | 0.0001 | - |
|
| 506 |
-
| 2.7050 | 8050 | 0.0 | - |
|
| 507 |
-
| 2.7218 | 8100 | 0.0 | - |
|
| 508 |
-
| 2.7386 | 8150 | 0.0001 | - |
|
| 509 |
-
| 2.7554 | 8200 | 0.0 | - |
|
| 510 |
-
| 2.7722 | 8250 | 0.0 | - |
|
| 511 |
-
| 2.7890 | 8300 | 0.0 | - |
|
| 512 |
-
| 2.8058 | 8350 | 0.0 | - |
|
| 513 |
-
| 2.8226 | 8400 | 0.0 | - |
|
| 514 |
-
| 2.8394 | 8450 | 0.0 | - |
|
| 515 |
-
| 2.8562 | 8500 | 0.0 | - |
|
| 516 |
-
| 2.8730 | 8550 | 0.0 | - |
|
| 517 |
-
| 2.8898 | 8600 | 0.0001 | - |
|
| 518 |
-
| 2.9066 | 8650 | 0.0001 | - |
|
| 519 |
-
| 2.9234 | 8700 | 0.0 | - |
|
| 520 |
-
| 2.9402 | 8750 | 0.0002 | - |
|
| 521 |
-
| 2.9570 | 8800 | 0.0 | - |
|
| 522 |
-
| 2.9738 | 8850 | 0.0001 | - |
|
| 523 |
-
| 2.9906 | 8900 | 0.0001 | - |
|
| 524 |
-
| 3.0074 | 8950 | 0.0001 | - |
|
| 525 |
-
| 3.0242 | 9000 | 0.0001 | - |
|
| 526 |
-
| 3.0410 | 9050 | 0.0 | - |
|
| 527 |
-
| 3.0578 | 9100 | 0.0 | - |
|
| 528 |
-
| 3.0746 | 9150 | 0.0001 | - |
|
| 529 |
-
| 3.0914 | 9200 | 0.0001 | - |
|
| 530 |
-
| 3.1082 | 9250 | 0.0001 | - |
|
| 531 |
-
| 3.125 | 9300 | 0.0 | - |
|
| 532 |
-
| 3.1418 | 9350 | 0.0 | - |
|
| 533 |
-
| 3.1586 | 9400 | 0.0001 | - |
|
| 534 |
-
| 3.1754 | 9450 | 0.0001 | - |
|
| 535 |
-
| 3.1922 | 9500 | 0.0 | - |
|
| 536 |
-
| 3.2090 | 9550 | 0.0 | - |
|
| 537 |
-
| 3.2258 | 9600 | 0.0 | - |
|
| 538 |
-
| 3.2426 | 9650 | 0.0 | - |
|
| 539 |
-
| 3.2594 | 9700 | 0.0 | - |
|
| 540 |
-
| 3.2762 | 9750 | 0.0002 | - |
|
| 541 |
-
| 3.2930 | 9800 | 0.0001 | - |
|
| 542 |
-
| 3.3098 | 9850 | 0.0 | - |
|
| 543 |
-
| 3.3266 | 9900 | 0.0 | - |
|
| 544 |
-
| 3.3434 | 9950 | 0.0 | - |
|
| 545 |
-
| 3.3602 | 10000 | 0.0 | - |
|
| 546 |
-
| 3.3770 | 10050 | 0.0001 | - |
|
| 547 |
-
| 3.3938 | 10100 | 0.0001 | - |
|
| 548 |
-
| 3.4106 | 10150 | 0.0 | - |
|
| 549 |
-
| 3.4274 | 10200 | 0.0 | - |
|
| 550 |
-
| 3.4442 | 10250 | 0.0001 | - |
|
| 551 |
-
| 3.4610 | 10300 | 0.0 | - |
|
| 552 |
-
| 3.4778 | 10350 | 0.1212 | - |
|
| 553 |
-
| 3.4946 | 10400 | 0.0001 | - |
|
| 554 |
-
| 3.5114 | 10450 | 0.0 | - |
|
| 555 |
-
| 3.5282 | 10500 | 0.1183 | - |
|
| 556 |
-
| 3.5450 | 10550 | 0.0 | - |
|
| 557 |
-
| 3.5618 | 10600 | 0.0002 | - |
|
| 558 |
-
| 3.5786 | 10650 | 0.0001 | - |
|
| 559 |
-
| 3.5954 | 10700 | 0.0 | - |
|
| 560 |
-
| 3.6122 | 10750 | 0.0 | - |
|
| 561 |
-
| 3.6290 | 10800 | 0.0001 | - |
|
| 562 |
-
| 3.6458 | 10850 | 0.0001 | - |
|
| 563 |
-
| 3.6626 | 10900 | 0.0 | - |
|
| 564 |
-
| 3.6794 | 10950 | 0.0 | - |
|
| 565 |
-
| 3.6962 | 11000 | 0.0 | - |
|
| 566 |
-
| 3.7130 | 11050 | 0.0 | - |
|
| 567 |
-
| 3.7298 | 11100 | 0.0 | - |
|
| 568 |
-
| 3.7466 | 11150 | 0.0 | - |
|
| 569 |
-
| 3.7634 | 11200 | 0.0 | - |
|
| 570 |
-
| 3.7802 | 11250 | 0.0 | - |
|
| 571 |
-
| 3.7970 | 11300 | 0.0 | - |
|
| 572 |
-
| 3.8138 | 11350 | 0.0 | - |
|
| 573 |
-
| 3.8306 | 11400 | 0.0 | - |
|
| 574 |
-
| 3.8474 | 11450 | 0.0 | - |
|
| 575 |
-
| 3.8642 | 11500 | 0.0001 | - |
|
| 576 |
-
| 3.8810 | 11550 | 0.0 | - |
|
| 577 |
-
| 3.8978 | 11600 | 0.0001 | - |
|
| 578 |
-
| 3.9147 | 11650 | 0.0 | - |
|
| 579 |
-
| 3.9315 | 11700 | 0.0001 | - |
|
| 580 |
-
| 3.9483 | 11750 | 0.0001 | - |
|
| 581 |
-
| 3.9651 | 11800 | 0.0001 | - |
|
| 582 |
-
| 3.9819 | 11850 | 0.0 | - |
|
| 583 |
-
| 3.9987 | 11900 | 0.0 | - |
|
| 584 |
|
| 585 |
-
|
| 586 |
-
|
| 587 |
-
|
| 588 |
-
|
| 589 |
-
|
| 590 |
-
|
| 591 |
-
|
| 592 |
-
|
| 593 |
|
| 594 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 595 |
|
| 596 |
-
|
| 597 |
-
```bibtex
|
| 598 |
-
@article{https://doi.org/10.48550/arxiv.2209.11055,
|
| 599 |
-
doi = {10.48550/ARXIV.2209.11055},
|
| 600 |
-
url = {https://arxiv.org/abs/2209.11055},
|
| 601 |
-
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
|
| 602 |
-
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
| 603 |
-
title = {Efficient Few-Shot Learning Without Prompts},
|
| 604 |
-
publisher = {arXiv},
|
| 605 |
-
year = {2022},
|
| 606 |
-
copyright = {Creative Commons Attribution 4.0 International}
|
| 607 |
-
}
|
| 608 |
-
```
|
| 609 |
|
| 610 |
-
|
| 611 |
-
## Glossary
|
| 612 |
|
| 613 |
-
|
| 614 |
-
-->
|
| 615 |
|
| 616 |
-
|
| 617 |
-
## Model Card Authors
|
| 618 |
|
| 619 |
-
|
| 620 |
-
-->
|
| 621 |
|
| 622 |
-
|
| 623 |
-
## Model Card Contact
|
| 624 |
|
| 625 |
-
|
| 626 |
-
-->
|
|
|
|
| 6 |
- text-classification
|
| 7 |
- generated_from_setfit_trainer
|
| 8 |
metrics:
|
| 9 |
+
- f1
|
| 10 |
+
- accuracy
|
| 11 |
widget:
|
| 12 |
+
- text: >-
|
| 13 |
+
A combined 20 million people per year die of smoking and hunger, so
|
| 14 |
+
authorities can't seem to feed people and they allow you to buy cigarettes
|
| 15 |
+
but we are facing another lockdown for a virus that has a 99.5% survival
|
| 16 |
+
rate!!! THINK PEOPLE. LOOK AT IT LOGICALLY WITH YOUR OWN EYES.
|
| 17 |
+
- text: >-
|
| 18 |
+
Scientists do not agree on the consequences of climate change, nor is there
|
| 19 |
+
any consensus on that subject. The predictions on that from are just
|
| 20 |
+
ascientific speculation. Bring on the warming."
|
| 21 |
+
- text: >-
|
| 22 |
+
If Tam is our "top doctor"....I am going back to leaches and voodoo...just
|
| 23 |
as much science in that as the crap she spouts
|
| 24 |
+
- text: "Can she skip school by herself and sit infront of parliament? \r\n Fake emotions and just a good actor."
|
|
|
|
| 25 |
- text: my dad had huge ones..so they may be real..
|
| 26 |
pipeline_tag: text-classification
|
| 27 |
+
inference: false
|
| 28 |
base_model: sentence-transformers/paraphrase-mpnet-base-v2
|
| 29 |
model-index:
|
| 30 |
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
|
|
|
|
| 38 |
split: test
|
| 39 |
metrics:
|
| 40 |
- type: metric
|
| 41 |
+
value: 0.688144336139226
|
| 42 |
name: Metric
|
| 43 |
+
license: mit
|
| 44 |
+
language:
|
| 45 |
+
- en
|
| 46 |
---
|
| 47 |
|
| 48 |
+
# Computational Analysis of Communicative Acts for Understanding Crisis News Comment Discourses
|
| 49 |
|
| 50 |
+
The official trained models for **"Computational Analysis of Communicative Acts for Understanding Crisis News Comment Discourses"**.
|
| 51 |
|
| 52 |
+
This model is based on **SetFit** ([SetFit: Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)) and uses the **sentence-transformers/paraphrase-mpnet-base-v2** pretrained model. It has been fine-tuned on our **crisis narratives dataset**.
|
| 53 |
|
| 54 |
+
---
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|
| 55 |
|
| 56 |
+
### Model Information
|
| 57 |
+
|
| 58 |
+
- **Architecture:** SetFit with sentence-transformers/paraphrase-mpnet-base-v2
|
| 59 |
+
- **Task:** Single-label classification for communicative act actions
|
| 60 |
+
- **Classes:**
|
| 61 |
+
- `informing statement`
|
| 62 |
+
- `challenge`
|
| 63 |
+
- `rejection`
|
| 64 |
+
- `appreciation`
|
| 65 |
+
- `request`
|
| 66 |
+
- `question`
|
| 67 |
+
- `acceptance`
|
| 68 |
+
- `apology`
|
| 69 |
+
- `evaluation`
|
| 70 |
+
- `proposal`
|
| 71 |
+
- `denial`
|
| 72 |
+
- `admission`
|
| 73 |
|
| 74 |
+
---
|
|
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|
| 75 |
|
| 76 |
+
### How to Use the Model
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|
| 77 |
|
| 78 |
+
You can find the code to fine-tune this model and detailed instructions in the following GitHub repository:
|
| 79 |
+
[Acts in Crisis Narratives - SetFit Fine-Tuning Notebook](https://github.com/Aalto-CRAI-CIS/Acts-in-crisis-narratives/blob/main/few_shot_learning/SetFit.ipynb)
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|
| 80 |
|
| 81 |
+
#### Steps to Load and Use the Model:
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|
| 82 |
|
| 83 |
+
1. Install the SetFit library:
|
| 84 |
+
```bash
|
| 85 |
+
pip install setfit
|
| 86 |
+
```
|
| 87 |
+
|
| 88 |
+
2. Load the model and run inference:
|
| 89 |
+
```python
|
| 90 |
+
from setfit import SetFitModel
|
| 91 |
|
| 92 |
+
# Download from the 🤗 Hub
|
| 93 |
+
model = SetFitModel.from_pretrained("CrisisNarratives/setfit-13classes-single_label")
|
| 94 |
+
|
| 95 |
+
# Run inference
|
| 96 |
+
preds = model("I'm sorry.")
|
| 97 |
+
```
|
| 98 |
|
| 99 |
+
For detailed instructions, refer to the GitHub repository linked above.
|
|
|
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|
| 100 |
|
| 101 |
+
---
|
|
|
|
| 102 |
|
| 103 |
+
### Citation
|
|
|
|
| 104 |
|
| 105 |
+
If you use this model in your work, please cite:
|
|
|
|
| 106 |
|
| 107 |
+
##### TO BE ADDED.
|
|
|
|
| 108 |
|
| 109 |
+
### Questions or Feedback?
|
|
|
|
| 110 |
|
| 111 |
+
For questions or feedback, please reach out via our [contact form](mailto:faezeghorbanpour96@example.com).
|
|
|