oluwatosin adewumi commited on
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1f69796
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Wolof model created.

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README.md ADDED
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+ ---
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+ thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
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+ language:
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+ - en
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+ license: cc-by-4.0
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+ tags:
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+ - conversational
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+ - transformers
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+ datasets:
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+ - multi_woz_v22 and AfriWOZ
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+ metrics:
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+ - perplexity
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+ widget:
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+ - text: "dedet li rek la soxla. jerejef. ba benen yoon."
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+ ---
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+
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+ ## DialoGPT_AfriWOZ
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+
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+ This is a fine-tuned model of DialoGPT (medium)-MutiWOZ on the AfriWOZ dataset. It is intended to be used as a conversational system in Wolof language.
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+ The dataset it's trained on is limited in scope, as it covers only certain domains such as restaurants, hotel, taxi, and booking.
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+
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+ The perplexity achieved on the test and validation sets are 20.74 and 11.66, respectively. More information about the original pre-trained model can be found [here](https://huggingface.co/tosin/dialogpt_mwoz)
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+
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+ * Generation example from an interactive environment:
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+
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+ |Role | Response |
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+ |---------|------------|
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+ |User | sowwu bi la bëg |
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+ |Bot | waw, ndax mën ngan ma jox ay leeral si bërëb buñu feetee ak lekkukaay bi |
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+ |User | soxla na dem gawu. ngir leeral rek bëg na joggee peterborough di dem cambridge |
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+ |Bot | soxlu na dem ginaw jurrom ñenti waxtu ak ñaar fukki simili ak jurrom ben. |
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+ |User | waw jiss sal ma ben bu seer si njëg si toluway |
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+ |Bot | jënd bi baaxna. jerejef. ba benen yoon. |
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+
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+ Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)
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+
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+ The paper for this work can be found on arXiv: [https://arxiv.org/pdf/2204.08083.pdf](https://arxiv.org/pdf/2204.08083.pdf)
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+
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+ ### How to use
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+
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+ Now we are ready to try out how the model works as a chatting partner!
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ import torch
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+ tokenizer = AutoTokenizer.from_pretrained("tosin/dialogpt_mwoz")
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+ model = AutoModelForCausalLM.from_pretrained("tosin/dialogpt_mwoz")
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+ # Let's chat for 5 lines
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+ for step in range(5):
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+ # encode the new user input, add the eos_token and return a tensor in Pytorch
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+ new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
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+ # append the new user input tokens to the chat history
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+ bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
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+ # generated a response while limiting the total chat history to 1000 tokens,
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+ chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
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+ # pretty print last ouput tokens from bot
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+ print("DialoGPT_wolof_Bot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
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+
config.json ADDED
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+ {
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+ "_name_or_path": "microsoft/DialoGPT-small",
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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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+ "conversational": {
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+ "max_length": 1000
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+ }
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+ },
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+ "transformers_version": "4.2.2",
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+ "use_cache": true,
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+ "vocab_size": 50257
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+ }
merges.txt ADDED
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