New model from https://wandb.ai/wandb/huggingtweets/runs/11n22kek
Browse files- README.md +12 -12
- config.json +3 -1
- pytorch_model.bin +1 -1
- training_args.bin +2 -2
README.md
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---
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language: en
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thumbnail: https://
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tags:
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- huggingtweets
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widget:
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<div class="inline-flex flex-col" style="line-height: 1.5;">
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<div class="flex">
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<div
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style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/
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</div>
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<div
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style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
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</div>
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</div>
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<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">π€ AI BOT π€</div>
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<div style="text-align: center; font-size: 16px; font-weight: 800">jamar "
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<div style="text-align: center; font-size: 14px;">@rxmaybike</div>
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</div>
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## Training data
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The model was trained on tweets from jamar "
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| Data | jamar "
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| --- | --- |
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| Tweets downloaded |
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| Retweets |
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| Short tweets |
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| Tweets kept |
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[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/
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## Training procedure
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The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @rxmaybike's tweets.
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Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/
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At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/
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## How to use
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---
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language: en
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thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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tags:
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- huggingtweets
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widget:
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<div class="inline-flex flex-col" style="line-height: 1.5;">
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<div class="flex">
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<div
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style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1454672063319392260/iwO_Ll7D_400x400.jpg')">
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</div>
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<div
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style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
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</div>
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</div>
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<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">π€ AI BOT π€</div>
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<div style="text-align: center; font-size: 16px; font-weight: 800">jamar " The Fool β majima π΅πΈ</div>
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<div style="text-align: center; font-size: 14px;">@rxmaybike</div>
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</div>
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## Training data
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The model was trained on tweets from jamar " The Fool β majima π΅πΈ.
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| Data | jamar " The Fool β majima π΅πΈ |
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| --- | --- |
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| Tweets downloaded | 3117 |
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| Retweets | 1859 |
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| Short tweets | 311 |
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| Tweets kept | 947 |
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[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/10shie54/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
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## Training procedure
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The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @rxmaybike's tweets.
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Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/11n22kek) for full transparency and reproducibility.
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At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/11n22kek/artifacts) is logged and versioned.
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## How to use
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config.json
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"n_inner": null,
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"n_layer": 12,
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"n_positions": 1024,
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"resid_pdrop": 0.1,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.
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"use_cache": true,
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"vocab_size": 50257
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}
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"n_inner": null,
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"n_layer": 12,
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"n_positions": 1024,
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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.1,
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"scale_attn_by_inverse_layer_idx": false,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.13.0",
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"use_cache": true,
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"vocab_size": 50257
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}
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pytorch_model.bin
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training_args.bin
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