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sujeshpadhi
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db58511
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Parent(s):
9e97375
uploaded config and gui files
Browse files- __init__.py +0 -0
- config.json +39 -0
- gui.py +47 -0
__init__.py
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config.json
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{
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"device": "cuda",
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"dataset": {
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"ids": [2],
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"cache_dir": "../dataset",
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"batch_size": 16,
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"num_workers": 8
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},
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"model": {
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"freeze_till": -1
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},
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"optim_args": {
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"lr": 1e-4
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},
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"trainer": {
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"limit_train_batches": 0.25,
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"max_epochs": 1,
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"deterministic": false,
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"log_every_n_steps": 2048,
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"accelerator": "gpu",
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"check_val_every_n_epoch": 1,
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"precision": "16-mixed",
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"enable_progress_bar": true,
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"default_root_dir": "./logs",
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"enable_checkpointing": true,
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"benchmark": true,
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"max_time": null
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},
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"weight": "./saved/models/T5-v4.pth",
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"fit": {
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"ckpt_path": null
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}
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}
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gui.py
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#installing the gradio transformer
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#!pip install -q gradio git+https://github.com/huggingface/transformers gradio torch
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import gradio as gr
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#from transformers import AutoModelForSeq2SeqLM, pipeline
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import torch
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import json
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from transformers import T5ForConditionalGeneration
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from model.T5 import T5
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# this model was loaded from https://hf.co/models
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model = T5('t5-small').to('cuda')
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LANGS = ["English", "German", "Italian", "Dutch", "Romanian", "French"]
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# Load the weights
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with open("config.json", "r") as f:
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config = json.load(f)
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model.load_state_dict(torch.load(config["weight"]))
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def translate(text, src_lang, tgt_lang):
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"""
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Translate the text from source lang to target lang
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"""
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inputs = ["translate "+src_lang+" to "+tgt_lang+": "+text]
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with torch.inference_mode():
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outputs = model.predict(inputs)
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return outputs[0]
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demo = gr.Interface(
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fn=translate,
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inputs=[
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gr.components.Textbox(label="Text"),
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gr.components.Dropdown(label="Source Language", choices=LANGS),
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gr.components.Dropdown(label="Target Language", choices=LANGS),
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],
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outputs=["text"],
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#examples=[["Building a translation demo with Gradio is so easy!", "eng_Latn", "spa_Latn"]],
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cache_examples=False,
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title="Language Translator",
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description="This is a GUI for the Language Translation System"
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)
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demo.launch(share=True)
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