New model from https://wandb.ai/wandb/huggingtweets/runs/3s7p5y1r
Browse files- README.md +75 -0
- config.json +40 -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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<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/1375299004414763009/2fzD8QOB_400x400.jpg')">
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</div>
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<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gabriel 🏳️🌈💦😈🌎🔥🥺 🤖 AI Bot </div>
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<div style="font-size: 15px">@glockmetal 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://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 [@glockmetal's tweets](https://twitter.com/glockmetal).
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| Data | Quantity |
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| --- | --- |
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| Tweets downloaded | 3206 |
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| Retweets | 290 |
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| Short tweets | 921 |
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| Tweets kept | 1995 |
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[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3dx8iokq/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 @glockmetal's tweets.
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Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3s7p5y1r) for full transparency and reproducibility.
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At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3s7p5y1r/artifacts) is logged and versioned.
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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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```python
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from transformers import pipeline
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generator = pipeline('text-generation',
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model='huggingtweets/glockmetal')
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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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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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[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)
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For more details, visit the project repository.
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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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"transformers_version": "4.4.2",
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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:e4bd766cca157782aca16740197d7e47a9efdbe98efd35a946ca2e51fe3a77bd
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size 510408315
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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, "special_tokens_map_file": null, "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 sha256:212fb2a57908a018f6a32fdb1414fc97a8cada0b613adc84c27a5943d4e5bb77
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size 2287
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vocab.json
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