Uploading model files

README.md

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+---
+license: apache-2.0
+
+datasets:
+- jfleg
+
+language:
+- en
+
+pipeline_tag: text2text-generation
+
+tags:
+- grammar correction
+---
+
+# Model
+
+This model utilizes the [Flan-T5-base](https://huggingface.co/google/flan-t5-base) pre-trained model and has been fine-tuned using the [JFLEG](https://huggingface.co/datasets/jfleg) dataset with the assistance of the [Happy Transformer](https://github.com/EricFillion/happy-transformer) framework. Its primary objective is to correct a wide range of potential grammatical errors that sentences might contain including issues with punctuation, typos, prepositions, and more.
+
+# Usage with Transformers
+
+```
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("Sajid030/t5-base-grammar-synthesis")
+model = AutoModelForSeq2SeqLM.from_pretrained("Sajid030/t5-base-grammar-synthesis")
+
+text = "One person if do n't have good health that means so many things they could lost ."
+inputs = tokenizer("grammar:"+text, truncation=True, return_tensors='pt')
+
+output = model.generate(inputs['input_ids'])
+correction=tokenizer.batch_decode(output, skip_special_tokens=True)
+print("".join(correction)) #Correction: If one person doesn't have good health, so many things could be lost.
+```
+
+# Usage with HappyTransformers
+
+```
+from happytransformer import HappyTextToText, TTSettings
+happy_tt = HappyTextToText("T5", "Sajid030/t5-base-grammar-synthesis")
+args = TTSettings()
+
+sentence = "Much many brands and sellers still in the market."
+result = happy_tt.generate_text("grammar: "+ sentence, args=args)
+
+print(result.text) # Many brands and sellers are still in the market.
+```

config.json

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+{
+  "_name_or_path": "Sajid030/t5-base-grammar-synthesis",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "max_length": 512,
+  "min_length": 2,
+  "model_type": "t5",
+  "n_positions": 512,
+  "no_repeat_ngram_size": 4,
+  "num_beams": 2,
+  "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,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.0",
+  "use_cache": true,
+  "vocab_size": 32100
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "max_new_tokens": 256,
+  "bos_token_id": 0,
+  "decoder_start_token_id": 2,
+  "early_stopping": true,
+  "eos_token_id": 2,
+  "forced_bos_token_id": 0,
+  "forced_eos_token_id": 2,
+  "no_repeat_ngram_size": 3,
+  "num_beams": 3,
+  "pad_token_id": 1,
+  "transformers_version": "4.33.3"
+}

pytorch_model.bin

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special_tokens_map.json

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tokenizer_config.json

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