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import os |
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import pandas as pd |
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import datasets |
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import gradio as gr |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline |
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CHECKPOINT_PATH= 'scratch_2-nodes_tokenizer_latbert-original_packing_fcocchi/' |
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CHECKPOINT_PATH= 'itserr/scratch_2-nodes_tokenizer_latbert-original_packing_fcocchi' |
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print(f"Loading model from: {CHECKPOINT_PATH}") |
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tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_PATH, token=os.environ['HF_TOKEN_READ']) |
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model = AutoModelForCausalLM.from_pretrained(CHECKPOINT_PATH, token=os.environ['HF_TOKEN_READ']) |
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preference_dataset_name= "itserr/latin_gpt_preferences" |
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global dataset_hf |
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dataset_hf = datasets.load_dataset(preference_dataset_name, token=os.environ['HF_TOKEN_READ'], download_mode='force_redownload') |
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dataset_hf = dataset_hf['train'].to_pandas() |
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print(dataset_hf.shape) |
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description=""" |
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This is a Latin Language Model (LLM) based on GPT-2 and it was trained on a large corpus of Latin texts and can generate text in Latin. \n |
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Demo instructions: |
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- Enter a prompt in Latin in the Input Text box. |
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- Select the temperature value to control the randomness of the generated text (higher value produce a more creative and unstable answer). |
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- Click the 'Generate Text' button to trigger model generation. |
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- (Optional) insert a Feedback text in the box. |
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- Click the 'Like' or 'Dislike' button to judge the generation correctness. |
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""" |
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title= "LatinGPT" |
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article= "hello world ..." |
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examples= ['Accidere ex una scintilla', 'Audacter calumniare,', 'Consolatium misero comites', 'Errare humanum est,', 'Excusatio non petita,'] |
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logo_image= 'ITSERR_row_logo.png' |
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def generate_text(prompt, slider): |
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if torch.cuda.is_available(): device = torch.device("cuda") |
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else: |
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device = torch.device("cpu") |
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print("No GPU available") |
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print("***** Generate *****") |
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text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device) |
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generated_text = text_generator(prompt, max_length=50, do_sample=True, temperature=slider, repetition_penalty=2.0, truncation=True) |
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return generated_text[0]['generated_text'] |
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def handle_preference(preference, input, output, feedback, temp_value): |
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""" |
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Format values stored in preferences: |
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- input text |
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- output generated text |
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- user feedback |
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- float temperature value |
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""" |
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global dataset_hf |
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if input == output: |
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output_tuple= ("", "") |
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else: |
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output_tuple= (input, output.split(input)[-1]) |
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if preference == "like": |
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dislike=0 |
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like=1 |
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count_like= dataset_hf.iloc[-1]['count_like'] |
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count_dislike= dataset_hf.iloc[-1]['count_dislike'] |
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if output_tuple[1] != "" : |
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count_like= dataset_hf.iloc[-1]['count_like'] + 1 |
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elif preference == "dislike": |
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dislike=1 |
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like=0 |
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count_like= dataset_hf.iloc[-1]['count_like'] |
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count_dislike= dataset_hf.iloc[-1]['count_dislike'] |
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if output_tuple[1] != "" : |
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count_dislike= dataset_hf.iloc[-1]['count_dislike'] + 1 |
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inp_text= output_tuple[0] |
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out_text= output_tuple[1] |
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new_data = pd.DataFrame({'input_text': inp_text, 'generated_text': out_text, 'feedback': feedback, |
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'temperature': float(temp_value), 'like': like, 'dislike': dislike, |
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'count_like': count_like, 'count_dislike': count_dislike}, index=[0]) |
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dataset_hf = pd.concat([dataset_hf, new_data], ignore_index=True) |
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hf_dataset = datasets.Dataset.from_pandas(dataset_hf) |
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dataset_dict = datasets.DatasetDict({"train": hf_dataset}) |
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dataset_dict.push_to_hub(preference_dataset_name, token=os.environ['HF_TOKEN_WRITE']) |
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print(f"Admin log: like: {count_like} and dislike: {count_dislike}") |
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return f"You select '{preference}' as answer of the model generation. Thank you for your time!" |
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custom_css = """ |
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#logo { |
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display: block; |
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margin-left: auto; |
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margin-right: auto; |
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width: 280px; |
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height: 140px; |
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} |
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""" |
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with gr.Blocks(css=custom_css) as demo: |
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gr.Image(logo_image, elem_id="logo") |
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gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>") |
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gr.Markdown(description) |
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with gr.Row(): |
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with gr.Column(): |
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input_text = gr.Textbox(lines=5, placeholder="Enter latin text here...", label="Input Text") |
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with gr.Column(): |
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output_text = gr.Textbox(lines=5, placeholder="Output text will appear here...", label="Output Text") |
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gr.Examples(examples=examples, inputs=input_text, cache_examples=True, fn=generate_text, outputs=output_text) |
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temperature_slider = gr.Slider(minimum=0.1, maximum=5.0, step=0.1, value=1.0, label="Temperature") |
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clean_button = gr.Button("Generate Text") |
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clean_button.click(fn=generate_text, inputs=[input_text, temperature_slider], outputs=output_text) |
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feedback_output = gr.Textbox(lines=1, placeholder="If you want to provide a feedback, please fill this box ...", label="Feedback") |
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with gr.Row(): |
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like_button = gr.Button("Like") |
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dislike_button = gr.Button("Dislike") |
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button_output = gr.Textbox(lines=1, placeholder="Please submit your choice", label="Latin Language Model Demo") |
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like_button.click(fn=lambda x,y,z,v: handle_preference("like", x, y, z, v), inputs=[input_text, output_text, feedback_output, temperature_slider], outputs=button_output) |
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dislike_button.click(fn=lambda x,y,z,v: handle_preference("dislike", x, y, z, v), inputs=[input_text, output_text, feedback_output, temperature_slider], outputs=button_output) |
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demo.launch(share=True) |
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