new file: app.py

new file: save_model/config.json
new file: save_model/generation_config.json
new file: save_model/pytorch_model.bin
new file: save_model/special_tokens_map.json
new file: save_model/spiece.model
new file: save_model/tokenizer_config.json

app.py

@@ -0,0 +1,32 @@
+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration,T5Model
+import gradio as gr
+
+def get_questions(paragraph, tokenizer, model, device):
+    bt_levels = ['Remember', 'Understand', 'Apply', 'Analyse', 'Evaluate', 'Create']
+    questions_dict = {}
+    for bt_level in bt_levels:
+        input_text = f'{bt_level}: {paragraph} {tokenizer.eos_token}'
+        input_ids = tokenizer.encode(input_text, max_length=512, padding='max_length', truncation=True, return_tensors='pt').to(device)
+        model.eval()
+        generated_ids = model.generate(input_ids, max_length=128, num_beams=4, early_stopping=True).to(device)
+        output_text = tokenizer.decode(generated_ids.squeeze(), skip_special_tokens=True).lstrip('\n')
+        output_text = output_text.split(' ', 1)[1]
+        questions_dict.update({bt_level: output_text})
+        # print(f'{bt_level} level question: {output_text}')
+    return questions_dict
+        
+
+def main(paragraph):
+    model = T5ForConditionalGeneration.from_pretrained('./save_model')
+    tokenizer = T5Tokenizer.from_pretrained('./save_model')
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    model.to(device)
+    output = get_questions(paragraph, tokenizer, model, device)
+    return output
+
+gr.Interface(
+    fn=main, 
+    inputs="textbox",
+    outputs="textbox",
+    live=True).launch()

save_model/config.json

@@ -0,0 +1,60 @@
+{
+  "_name_or_path": "t5-base",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.27.3",
+  "use_cache": true,
+  "vocab_size": 32128
+}

save_model/generation_config.json

@@ -0,0 +1,7 @@
+{
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+  "decoder_start_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.27.3"
+}

save_model/pytorch_model.bin

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save_model/special_tokens_map.json

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save_model/spiece.model

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save_model/tokenizer_config.json

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+}