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| import os | |
| os.system("pip install transformers") | |
| os.system("pip3 install torch==1.10.1+cpu torchvision==0.11.2+cpu torchaudio==0.10.1+cpu -f " | |
| "https://download.pytorch.org/whl/cpu/torch_stable.html") | |
| os.system("pip install mtranslate") | |
| os.system("pip install requests") | |
| os.system("pip install random") | |
| import transformers | |
| import json | |
| import random | |
| import requests | |
| from mtranslate import translate | |
| import streamlit as st | |
| MODELS = { | |
| "GPT-2 Model Recycled From English": { | |
| "url": "https://api-inference.huggingface.co/models/GroNLP/gpt2-small-dutch" | |
| }, | |
| } | |
| PROMPT_LIST = { | |
| "Er was eens...": ["Er was eens..."], | |
| "Dag.": ["Hallo, mijn naam is "], | |
| "Te zijn of niet te zijn?": ["Naar mijn mening is 'zijn'"], | |
| } | |
| def query(payload, model_name): | |
| data = json.dumps(payload) | |
| print("model url:", MODELS[model_name]["url"]) | |
| response = requests.request( | |
| "POST", MODELS[model_name]["url"], headers={}, data=data | |
| ) | |
| return json.loads(response.content.decode("utf-8")) | |
| def process( | |
| text: str, model_name: str, max_len: int, temp: float, top_k: int, top_p: float | |
| ): | |
| payload = { | |
| "inputs": text, | |
| "parameters": { | |
| "max_new_tokens": max_len, | |
| "top_k": top_k, | |
| "top_p": top_p, | |
| "temperature": temp, | |
| "repetition_penalty": 2.0, | |
| }, | |
| "options": { | |
| "use_cache": True, | |
| }, | |
| } | |
| return query(payload, model_name) | |
| # Page | |
| st.set_page_config(page_title="Dutch GPT-2 Demo") | |
| st.title("Dutch GPT-2") | |
| # Sidebar | |
| st.sidebar.subheader("Configurable parameters") | |
| max_len = st.sidebar.number_input( | |
| "Maximum length", | |
| value=100, | |
| help="The maximum length of the sequence to be generated.", | |
| ) | |
| temp = st.sidebar.slider( | |
| "Temperature", | |
| value=1.0, | |
| min_value=0.1, | |
| max_value=100.0, | |
| help="The value used to module the next token probabilities.", | |
| ) | |
| top_k = st.sidebar.number_input( | |
| "Top k", | |
| value=10, | |
| help="The number of highest probability vocabulary tokens to keep for top-k-filtering.", | |
| ) | |
| top_p = st.sidebar.number_input( | |
| "Top p", | |
| value=0.95, | |
| help=" If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.", | |
| ) | |
| do_sample = st.sidebar.selectbox( | |
| "Sampling?", | |
| (True, False), | |
| help="Whether or not to use sampling; use greedy decoding otherwise.", | |
| ) | |
| # Body | |
| st.markdown( | |
| """ | |
| Dutch GPT-2 model (small) is based on the English GPT-2 model: | |
| Researches [Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) and [M. Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475) | |
| obtained this model by transfering the English GPT-2 model in multiple procedure while exploiting genetic closeness between Dutch and English. | |
| During this process, they retrained the lexical embeddings of the original English GPT-2 model and did additional fine-tuning of the full Dutch model | |
| for better text generation. | |
| For more information on the model: | |
| [arXiv](https://arxiv.org/abs/2012.05628) | |
| [GitHub](https://github.com/wietsedv/gpt2-recycle) | |
| """ | |
| ) | |
| model_name = st.selectbox("Model", (list(MODELS.keys()))) | |
| ALL_PROMPTS = list(PROMPT_LIST.keys()) + ["Custom"] | |
| prompt = st.selectbox("Prompt", ALL_PROMPTS, index=len(ALL_PROMPTS) - 1) | |
| if prompt == "Custom": | |
| prompt_box = "Enter your text here" | |
| else: | |
| prompt_box = random.choice(PROMPT_LIST[prompt]) | |
| text = st.text_area("Enter text", prompt_box) | |
| if st.button("Run"): | |
| with st.spinner(text="Getting results..."): | |
| st.subheader("Result") | |
| print(f"maxlen:{max_len}, temp:{temp}, top_k:{top_k}, top_p:{top_p}") | |
| result = process( | |
| text=text, | |
| model_name=model_name, | |
| max_len=int(max_len), | |
| temp=temp, | |
| top_k=int(top_k), | |
| top_p=float(top_p), | |
| ) | |
| print("result:", result) | |
| if "error" in result: | |
| if type(result["error"]) is str: | |
| st.write(f'{result["error"]}.', end=" ") | |
| if "estimated_time" in result: | |
| st.write( | |
| f'Please try again in about {result["estimated_time"]:.0f} seconds.' | |
| ) | |
| else: | |
| if type(result["error"]) is list: | |
| for error in result["error"]: | |
| st.write(f"{error}") | |
| else: | |
| result = result[0]["generated_text"] | |
| st.write(result.replace("\n", " \n")) | |
| st.text("English translation") | |
| st.write(translate(result, "en", "nl").replace("\n", " \n")) | |
| st.subheader("References:") | |
| st.markdown( | |
| """ | |
| ``` | |
| @inproceedings{de-vries-nissim-2021-good, | |
| title = "As Good as New. How to Successfully Recycle {E}nglish {GPT}-2 to Make Models for Other Languages", | |
| author = "de Vries, Wietse and | |
| Nissim, Malvina", | |
| booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", | |
| month = aug, | |
| year = "2021", | |
| address = "Online", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://aclanthology.org/2021.findings-acl.74", | |
| doi = "10.18653/v1/2021.findings-acl.74", | |
| pages = "836--846", | |
| } | |
| ``` | |
| """ | |
| ) |