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PiGrieco/OpenSesame

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README.md CHANGED
@@ -16,36 +16,23 @@ should probably proofread and complete it, then remove this comment. -->
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  # OpenSesame
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- This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the [this](https://assets-eu.researchsquare.com/files/rs-2370113/v1/090f670e-87cd-40e8-8bf1-51fa9c02d105.pdf?c=1671622196) dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2134
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- - Accuracy: 0.9469
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- - F1: 0.9574
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  ## Model description
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- This model is part of "Word Of Prompt" library and is intended to detect buying intention in LLM prompt from users.
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- Label 1 = User has buying intention ;
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- Label 0 = User hasn't buying intention
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- # Join The Team
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- You can find our pitch deck here: [Word Of Prompt - Pitch Deck](https://drive.google.com/file/d/16EdcmIdPV7qq4XihTqDHyvNdUW-N7v4A/view?usp=sharing)
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- Word of Prompt wants to help LLM and Agent democratization: incertain returns on development of such technologies stop their diffusion.
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- WoP gives to developers an alternative monetization methodology, meanwhile enhances User Experience: revolutionizing advertising by integrating it seamlessly into AI-driven conversations, enhancing user experience while maintaining the natural flow of dialogue.
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- Unlike traditional disruptive ads, our integrable library leverages AI to present contextually relevant ads, mirroring the trust and personal touch of word-of-mouth recommendations.
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- This approach ensures that ads are not just seen but are also relevant and timely, significantly increasing engagement and conversion rates.
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- In future, we'll develop a managed platform giving marketers a new channel to marketing products and developers a new earning opportunity.
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- With Word of Prompt, we’re not just changing how ads are delivered; we’re transforming how they're perceived, making them a valuable addition to every conversation.
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- If you want to know more or join the team, contact us on LinkedIn: [Piermatteo Grieco](https://www.linkedin.com/in/piermatteo-grieco/)
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  ## Training procedure
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@@ -58,21 +45,25 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 4
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | 0.6007 | 1.0 | 85 | 0.5717 | 0.6681 | 0.6753 |
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- | 0.7874 | 2.0 | 170 | 0.9614 | 0.7080 | 0.8081 |
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- | 0.3041 | 3.0 | 255 | 0.2079 | 0.9381 | 0.9504 |
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- | 0.2707 | 4.0 | 340 | 0.2134 | 0.9469 | 0.9574 |
 
 
 
 
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  ### Framework versions
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- - Transformers 4.41.0
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  - Pytorch 2.3.0+cu121
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- - Datasets 2.19.1
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  - Tokenizers 0.19.1
 
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  # OpenSesame
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+ This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0985
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+ - Accuracy: 0.9795
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+ - F1: 0.9798
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  ## Model description
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+ More information needed
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+ ## Intended uses & limitations
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+ More information needed
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+ ## Training and evaluation data
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+ More information needed
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Training procedure
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 8
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | 0.629 | 1.0 | 129 | 0.4584 | 0.8187 | 0.8360 |
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+ | 0.5446 | 2.0 | 258 | 0.4895 | 0.8275 | 0.8435 |
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+ | 0.5951 | 3.0 | 387 | 0.3360 | 0.8830 | 0.8895 |
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+ | 0.4456 | 4.0 | 516 | 0.2366 | 0.9327 | 0.9341 |
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+ | 0.4282 | 5.0 | 645 | 0.1552 | 0.9591 | 0.9605 |
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+ | 0.2033 | 6.0 | 774 | 0.1302 | 0.9678 | 0.9687 |
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+ | 0.1816 | 7.0 | 903 | 0.0925 | 0.9825 | 0.9827 |
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+ | 0.1283 | 8.0 | 1032 | 0.0985 | 0.9795 | 0.9798 |
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  ### Framework versions
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+ - Transformers 4.41.2
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  - Pytorch 2.3.0+cu121
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+ - Datasets 2.19.2
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  - Tokenizers 0.19.1
config.json CHANGED
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  "position_embedding_type": "absolute",
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  "problem_type": "single_label_classification",
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  "torch_dtype": "float32",
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- "transformers_version": "4.41.0",
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  "type_vocab_size": 1,
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  "use_cache": true,
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  "vocab_size": 50265
 
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  "position_embedding_type": "absolute",
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  "problem_type": "single_label_classification",
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  "torch_dtype": "float32",
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+ "transformers_version": "4.41.2",
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  "type_vocab_size": 1,
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  "use_cache": true,
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  "vocab_size": 50265
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