Add application file

Requriements.txt

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+streamlit
+transformers
+Numpy
+pandas
+torch

config.json

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+{
+  "_name_or_path": "microsoft/biogpt",
+  "activation_dropout": 0.0,
+  "architectures": [
+    "BioGptForCausalLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "layerdrop": 0.0,
+  "max_position_embeddings": 1024,
+  "model_type": "biogpt",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 1,
+  "scale_embedding": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.27.4",
+  "use_cache": true,
+  "vocab_size": 42384
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "pad_token_id": 1,
+  "transformers_version": "4.27.4"
+}

special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

streamlit.py

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+
+
+%%writefile app.py
+import streamlit as st
+
+st.title("HEALTHQUERY")
+import os
+import io
+import requests
+import numpy as np
+import pandas as pd
+import re
+import zipfile
+import random
+import time
+import csv
+import datetime
+from itertools import compress
+from collections import Counter, defaultdict
+from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+from transformers import AutoTokenizer, AutoConfig, AutoModelForPreTraining, \
+                         AdamW, get_linear_schedule_with_warmup, \
+                         TrainingArguments, BeamScorer, Trainer
+
+import torch
+from torch.utils.data import Dataset, random_split, DataLoader, \
+                             RandomSampler, SequentialSampler
+
+from IPython.display import clear_output
+from transformers import BioGptTokenizer, BioGptForCausalLM, TrainerCallback
+from transformers import pipeline
+#summarizer_bart = pipeline("summarization", model="facebook/bart-large-cnn")
+#summarizer_knnkar = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
+summarizer_sshle = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+
+import os
+DEBUG           = False
+
+INPUT_DIR       = 'articles'
+
+USE_APEX        = True
+APEX_OPT_LEVEL  = 'O1'
+
+MODEL           = 'gpt2' #{gpt2, gpt2-medium, gpt2-large, gpt2-xl}
+
+UNFREEZE_LAST_N = 6 #The last N layers to unfreeze for training
+
+SPECIAL_TOKENS  = { "bos_token": "<|BOS|>",
+                    "eos_token": "<|EOS|>",
+                    "unk_token": "<|UNK|>",
+                    "pad_token": "<|PAD|>",
+                    "sep_token": "<|SEP|>"}
+
+MAXLEN          = 256  #{768, 1024, 1280, 1600}
+
+TRAIN_SIZE      = 0.8
+
+if USE_APEX:
+    TRAIN_BATCHSIZE = 16
+    BATCH_UPDATE    = 128
+else:
+    TRAIN_BATCHSIZE = 8
+    BATCH_UPDATE    = 256
+
+EPOCHS          = 3
+LR              = 5e-4
+EPS             = 1e-8
+WARMUP_STEPS    = 1e2
+
+SEED            = 2020
+
+
+DEVIDE_BY = 20
+
+os.environ['WANDB_DISABLED'] = 'true'
+
+
+
+tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
+model = BioGptForCausalLM.from_pretrained('/content/drive/MyDrive/All models/biogpt')
+
+
+
+input_text = st.text_input("Please Provide your text:")
+title = input_text
+prompt = SPECIAL_TOKENS['bos_token'] + title + SPECIAL_TOKENS['sep_token']
+generated = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)
+device = torch.device("cuda")
+generated = generated.to(device)
+device = torch.device("cuda")
+model.cuda()
+model.eval();
+from heapq import nsmallest
+
+# Generate text
+
+if len(input_text)>0:
+  sample_outputs = model.generate(generated,
+                                do_sample=True,
+                                max_length=MAXLEN,
+                                top_k=10,
+                                top_p=0.7,
+                                temperature=0.5,
+                                repetition_penalty=2.0,
+                                num_return_sequences=1
+                                )
+
+
+  # Initialize an empty list to store the perplexity and text pairs
+  perplexity_text_pairs = []
+
+
+  for i, sample_output in enumerate(sample_outputs):
+      text = tokenizer.decode(sample_output,skip_special_tokens=True)
+      a = len(title)+25
+      st.write(a)
+      st.write(("{}: {}\n\n".format(i+1,  text[a:])))
+      # all questions print in above cod
+  bart_Val=text[a:]
+  #x=summarizer(bart_Val, max_length=200, min_length=30, do_sample=False)
+  #st.write('-------Bart summarization-----')
+  #st.write(x[0]['summary_text'])
+
+  #summary=summarizer_knnkar(bart_Val, max_length=200, min_length=30, do_sample=False)
+  #st.write('-------MEETING_SUMMARY-----')
+  #st.write(summary[0]['summary_text'])
+
+  distl=summarizer_sshle(bart_Val, max_length=200, min_length=30, do_sample=False)
+  st.write('-------distilbart_cnn_12-6 model -----')
+  st.write(distl[0]['summary_text'])
+
+
+
+else:
+  st.write('Welcome to GPT2')
+# Create a "Regenerate" button
+
+
+# Display output
+

tokenizer_config.json

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+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "model_max_length": 1024,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "special_tokens_map_file": null,
+  "tokenizer_class": "BioGptTokenizer",
+  "unk_token": "<unk>"
+}

training_args (1).bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5b09e76b23eb79b374880ad63eb99198f4a78cbed763a96e46e218d6c593e787
+size 3579