New model from https://wandb.ai/wandb/huggingtweets/runs/3q94y8me
Browse files- README.md +111 -0
- config.json +39 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer_config.json +1 -0
- training_args.bin +3 -0
- vocab.json +0 -0
README.md
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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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<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
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<style>
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@media (prefers-color-scheme: dark) {
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.prose { color: #E2E8F0 !important; }
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.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
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}
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</style>
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<section class='prose'>
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<div>
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<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/953263245678215168/gKWkzY_f_400x400.jpg')">
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</div>
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<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Nathan Law 羅冠聰 😷 🤖 AI Bot </div>
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<div style="font-size: 15px; color: #657786">@nathanlawkc bot</div>
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</div>
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I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
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Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
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## How does it work?
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The model uses the following pipeline.
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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://app.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 [@nathanlawkc's tweets](https://twitter.com/nathanlawkc).
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<table style='border-width:0'>
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<thead style='border-width:0'>
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<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
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<th style='border-width:0'>Data</th>
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<th style='border-width:0'>Quantity</th>
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</tr>
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</thead>
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<tbody style='border-width:0'>
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<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
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<td style='border-width:0'>Tweets downloaded</td>
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<td style='border-width:0'>2786</td>
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</tr>
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<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
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<td style='border-width:0'>Retweets</td>
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<td style='border-width:0'>996</td>
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</tr>
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<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
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<td style='border-width:0'>Short tweets</td>
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<td style='border-width:0'>463</td>
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</tr>
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<tr style='border-width:0'>
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<td style='border-width:0'>Tweets kept</td>
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<td style='border-width:0'>1327</td>
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</tr>
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</tbody>
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</table>
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[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3svb5x6n/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 @nathanlawkc's tweets.
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Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3q94y8me) for full transparency and reproducibility.
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At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3q94y8me/artifacts) is logged and versioned.
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## Intended uses & limitations
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### How to use
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You can use this model directly with a pipeline for text generation:
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<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
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generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
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model=<span style="color:#FF9800">'huggingtweets/nathanlawkc'</span>)
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generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
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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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In addition, the data present in the user's tweets further affects the text generated by the model.
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## About
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*Built by Boris Dayma*
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</section>
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[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)
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<section class='prose'>
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For more details, visit the project repository.
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</section>
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[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
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config.json
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{
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"_name_or_path": "gpt2",
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2LMHeadModel"
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],
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"attn_pdrop": 0.1,
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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_ctx": 1024,
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"n_embd": 768,
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"n_head": 12,
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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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"summary_type": "cls_index",
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"summary_use_proj": true,
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"task_specific_params": {
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"text-generation": {
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"do_sample": true,
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"max_length": 160,
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"min_length": 10,
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"prefix": "<|endoftext|>",
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"temperature": 1.0,
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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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merges.txt
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:5147dab110c6c7c7fe914a15d37059021efc3287a720659c5ebff97b53a27f1d
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size 510406551
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special_tokens_map.json
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{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}
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tokenizer_config.json
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{"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "add_prefix_space": false, "model_max_length": 1024, "name_or_path": "gpt2"}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:5046096e36d0be3b51d67aeaf4ec2da78c44fd8eb0c3ce4b6c3b2a19a18540b7
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size 1775
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vocab.json
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