PiGrieco/OpenSesame
Browse files- README.md +20 -29
- config.json +1 -1
- logs/events.out.tfevents.1717783671.e3a39cc1c013.424.6 +3 -0
- model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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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
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.
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- F1: 0.
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## Model description
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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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- 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:
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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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### Framework versions
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- Transformers 4.41.
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.
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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
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config.json
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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.
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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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logs/events.out.tfevents.1717783671.e3a39cc1c013.424.6
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model.safetensors
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training_args.bin
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