Upload 8 files

MathT5.py

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+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+def pretty_print(text, prompt=True):
+    s = ""
+    if prompt:
+        for section in text.split(', '):
+            premises = section.split(" and ")
+            if len(premises) > 1:
+                for premise in premises[:-1]:
+                    s += premise + "\n\n\n" + "and" + "\n\n\n"
+                s += premises[-1] + "\n\n\n"
+            else:
+                s += section + "\n\n\n"
+    else:
+        for equation in text.split("and"):
+            s += equation + "\n\n\n"
+    return print(s[:-2])
+
+
+def load_model(model_id):
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    tokenizer = T5Tokenizer.from_pretrained(model_id)
+    model = T5ForConditionalGeneration.from_pretrained(model_id).to(device)
+    return tokenizer, model
+
+
+def inference(prompt, tokenizer, model):
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    input_ids = tokenizer.encode(prompt, return_tensors='pt', max_length=512, truncation=True).to(device)
+    output = model.generate(input_ids=input_ids, max_length=512, early_stopping=True)
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    # post-processing
+    derivation = generated_text.replace("\\ ","\\")
+    partial_symbols = derivation.split(" ")
+    backslash_syms = set([i for i in partial_symbols if "\\" in i])
+    for i in range(len(partial_symbols)):
+        sym = partial_symbols[i]
+        for b_sym in backslash_syms:
+            if b_sym.replace("\\","") == sym:
+                partial_symbols[i] = b_sym
+    return " ".join(partial_symbols)

added_tokens.json

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+{
+  "\\": 32100,
+  "^": 32103,
+  "{": 32101,
+  "}": 32102
+}

config.json

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+{
+  "_name_or_path": "/home/e87596jm/Generation/outputs/model_files/",
+  "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": "4.27.0",
+  "use_cache": true,
+  "vocab_size": 32104
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "decoder_start_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.27.0"
+}

pytorch_model.bin

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special_tokens_map.json

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spiece.model

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tokenizer_config.json

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+}