New model from https://wandb.ai/wandb/huggingtweets/runs/17zjqoal
Browse files- README.md +25 -15
- config.json +2 -1
- flax_model.msgpack +0 -3
- pytorch_model.bin +1 -1
- tokenizer.json +0 -0
- 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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- text: "My dream is"
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---
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<div>
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<div
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</div>
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I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
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## Training data
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The model was trained on
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| Data |
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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 @marknorm'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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- text: "My dream is"
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---
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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/903769803768217600/EKtan_aM_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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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">🤖 AI BOT 🤖</div>
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<div style="text-align: center; font-size: 16px; font-weight: 800">mark normand</div>
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<div style="text-align: center; font-size: 14px;">@marknorm</div>
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</div>
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I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
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## Training data
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The model was trained on tweets from mark normand.
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| Data | mark normand |
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| --- | --- |
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| Tweets downloaded | 3249 |
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| Retweets | 136 |
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| Short tweets | 522 |
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| Tweets kept | 2591 |
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[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/25e2ma2z/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 @marknorm's tweets.
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Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/17zjqoal) for full transparency and reproducibility.
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At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/17zjqoal/artifacts) is logged and versioned.
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## How to use
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config.json
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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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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"top_p": 0.95
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}
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},
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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_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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"summary_proj_to_labels": true,
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"top_p": 0.95
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}
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},
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"transformers_version": "4.6.1",
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"use_cache": true,
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"vocab_size": 50257
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}
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flax_model.msgpack
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size 497764120
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pytorch_model.bin
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tokenizer.json
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training_args.bin
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size 2415
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