New model from https://wandb.ai/wandb/huggingtweets/runs/2sprqk7j
Browse files- README.md +27 -19
- config.json +5 -1
- flax_model.msgpack +0 -3
- pytorch_model.bin +2 -2
- special_tokens_map.json +5 -1
- tokenizer.json +0 -0
- tokenizer_config.json +10 -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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- 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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</div>
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I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
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![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)
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To understand how the model was developed, check the [W&B report](https://
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## Training data
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The model was trained on
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| Data |
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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 @realdonaldtrump'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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generator("My dream is", num_return_sequences=5)
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```
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## Limitations and bias
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The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
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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/874276197357596672/kUuht00m_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">Donald J. Trump</div>
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<div style="text-align: center; font-size: 14px;">@realdonaldtrump</div>
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</div>
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I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
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![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)
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To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
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## Training data
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The model was trained on tweets from Donald J. Trump.
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| Data | Donald J. Trump |
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| --- | --- |
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| Tweets downloaded | 3050 |
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| Retweets | 958 |
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| Short tweets | 518 |
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| Tweets kept | 1574 |
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[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1o4eds23/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 @realdonaldtrump's tweets.
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Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2sprqk7j) for full transparency and reproducibility.
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At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2sprqk7j/artifacts) is logged and versioned.
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## How to use
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generator("My dream is", num_return_sequences=5)
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```
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## Limitations and bias
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The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
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config.json
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"bos_token_id": 50256,
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"embd_pdrop": 0.1,
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"eos_token_id": 50256,
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"gradient_checkpointing": false,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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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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"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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"use_cache": true,
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"vocab_size": 50257
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}
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"bos_token_id": 50256,
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"embd_pdrop": 0.1,
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"eos_token_id": 50256,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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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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"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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"torch_dtype": "float32",
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"transformers_version": "4.25.1",
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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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pytorch_model.bin
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special_tokens_map.json
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{
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{
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"unk_token": "<|endoftext|>"
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}
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tokenizer.json
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tokenizer_config.json
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{
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{
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"add_prefix_space": false,
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"model_max_length": 1024,
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"name_or_path": "gpt2",
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"special_tokens_map_file": null,
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"tokenizer_class": "GPT2Tokenizer",
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"unk_token": "<|endoftext|>"
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}
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training_args.bin
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