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app.py
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import gradio as gr
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from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from transformers import PreTrainedTokenizerFast
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import os
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# --------------------------------------
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# LOAD TOKENIZER
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# --------------------------------------
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TOKENIZER_JSON = "tokenizer_hindi_bpe_8k_stream/tokenizer.json"
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HF_DIR = "tokenizer_hindi_bpe_8k_stream/hf"
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if os.path.exists(HF_DIR):
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tokenizer = PreTrainedTokenizerFast.from_pretrained(HF_DIR)
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elif os.path.exists(TOKENIZER_JSON):
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tokenizer = PreTrainedTokenizerFast(tokenizer_file=TOKENIZER_JSON)
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else:
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raise ValueError("Tokenizer not found!")
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print("Tokenizer loaded: vocab =", tokenizer.vocab_size)
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# --------------------------------------
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# ENCODE / DECODE FUNCTIONS
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# --------------------------------------
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def encode_text(text: str):
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"""Basic encode: returns tokens + ids."""
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enc = tokenizer(text, add_special_tokens=False)
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tokens = tokenizer.convert_ids_to_tokens(enc["input_ids"])
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# return tokens, enc["input_ids"]
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csv_ids = ",".join(str(x) for x in enc["input_ids"])
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return tokens, csv_ids
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def encode_plus(text: str):
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enc = tokenizer(
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text,
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truncation=False,
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return_attention_mask=True,
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return_offsets_mapping=True,
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add_special_tokens=True
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)
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enc["input_ids_csv"] = ",".join(str(x) for x in enc["input_ids"])
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return enc
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def decode_ids(ids: str):
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"""Decode from comma-separated IDs to text."""
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try:
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arr = [int(x) for x in ids.split(",") if x.strip()]
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return tokenizer.decode(arr)
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except:
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return "❌ Invalid ID list"
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def batch_encode(text_list):
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"""Batch encode multiple lines separated by newline."""
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lines = [ln.strip() for ln in text_list.split("\n") if ln.strip()]
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enc = tokenizer(lines, add_special_tokens=False)
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out = []
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for i, ids in enumerate(enc["input_ids"]):
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toks = tokenizer.convert_ids_to_tokens(ids)
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out.append({
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"input": lines[i],
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"tokens": toks,
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"ids_csv": ",".join(str(x) for x in ids)
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})
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return out
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# --------------------------------------
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# FASTAPI REST BACKEND
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# --------------------------------------
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api = FastAPI(title="Hindi Tokenizer API")
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api.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_methods=["*"],
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allow_headers=["*"]
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)
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@api.get("/")
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def home():
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return {
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"message": "Hindi Tokenizer API",
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"vocab_size": tokenizer.vocab_size
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}
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@api.get("/tokenize")
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def tokenize_endpoint(text: str):
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enc = tokenizer(text, add_special_tokens=False)
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tokens = tokenizer.convert_ids_to_tokens(enc["input_ids"])
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return {"tokens": tokens, "ids": enc["input_ids"]}
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@api.get("/decode")
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def decode_endpoint(ids: str):
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try:
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arr = [int(x) for x in ids.split(",") if x.strip()]
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return {"text": tokenizer.decode(arr)}
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except:
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return {"error": "Invalid id list"}
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# --------------------------------------
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# GRADIO FRONTEND
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# --------------------------------------
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with gr.Blocks(title="Hindi Tokenizer") as demo:
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gr.Markdown("## 🔡 Hindi BPE Tokenizer — Encode / Decode / Batch")
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with gr.Tab("Encode"):
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text_in = gr.Textbox(label="Enter text")
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tokens_out = gr.JSON(label="Tokens")
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ids_out = gr.Textbox(label="Token IDs (CSV)")
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btn = gr.Button("Encode")
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btn.click(encode_text, text_in, [tokens_out, ids_out])
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with gr.Tab("Encode+ (HF full)"):
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text2_in = gr.Textbox(label="Enter text (HF encode_plus)")
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enc_plus_out = gr.JSON(label="Output")
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btn2 = gr.Button("Run encode_plus")
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btn2.click(encode_plus, text2_in, enc_plus_out)
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with gr.Tab("Decode"):
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ids_in = gr.Textbox(label="Comma-separated token IDs")
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text_out = gr.Textbox(label="Decoded text")
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btn3 = gr.Button("Decode")
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btn3.click(decode_ids, ids_in, text_out)
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with gr.Tab("Batch Encode"):
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batch_in = gr.Textbox(
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label="Enter multiple lines (newline separated)",
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placeholder="Line 1\nLine 2\nLine 3"
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)
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batch_out = gr.JSON(label="Batch output (CSV per line)")
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btn4 = gr.Button("Batch Encode")
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btn4.click(batch_encode, batch_in, batch_out)
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# Mount FastAPI + Gradio
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if "app" not in globals():
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app = gr.mount_gradio_app(api, demo, path="/gradio")
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if __name__ == "__main__":
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demo.launch(server_port=7860, share=False)
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requirements.txt
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@@ -0,0 +1,6 @@
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gradio
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fastapi
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uvicorn
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transformers
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tokenizers
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torch --index-url https://download.pytorch.org/whl/cpu
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tokenizer_hindi_bpe_8k_stream/hf/special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer_hindi_bpe_8k_stream/hf/tokenizer.json
ADDED
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The diff for this file is too large to render.
See raw diff
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tokenizer_hindi_bpe_8k_stream/hf/tokenizer_config.json
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@@ -0,0 +1,52 @@
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"3": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"4": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": false,
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"tokenizer_class": "PreTrainedTokenizerFast",
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"unk_token": "[UNK]"
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}
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tokenizer_hindi_bpe_8k_stream/tokenizer.json
ADDED
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The diff for this file is too large to render.
See raw diff
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