Create README.md

README.md

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+---
+language: en
+tags:
+- generative qa
+datasets:
+- eli5
+- stackexchange(pets, cooking, gardening, diy, crafts)
+---
+
+Work by [Frederico Vicente](https://huggingface.co/mrvicente) & [Diogo Tavares](https://huggingface.co/d-c-t). We finetuned BART Large for the task of generative question answering. It was trained on eli5, askScience and stackexchange using the following forums: pets, cooking, gardening, diy, crafts. 
+
+### Usage
+
+```python
+
+from transformers import (
+      BartForConditionalGeneration,
+      BartTokenizer
+)
+import torch
+import json
+
+def read_json_file_2_dict(filename, store_dir='.'):
+    with open(f'{store_dir}/{filename}', 'r', encoding='utf-8') as file:
+        return json.load(file)
+
+def get_device():
+    # If there's a GPU available...
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+        n_gpus = torch.cuda.device_count()
+        first_gpu = torch.cuda.get_device_name(0)
+
+        print(f'There are {n_gpus} GPU(s) available.')
+        print(f'GPU gonna be used: {first_gpu}')
+    else:
+        print('No GPU available, using the CPU instead.')
+        device = torch.device("cpu")
+    return device
+
+model_name = 'unlisboa/bart_qa_assistant'
+tokenizer = BartTokenizer.from_pretrained(model_name)
+device = get_device()
+model = BartForConditionalGeneration.from_pretrained(model_name).to(device)
+model.eval()
+                                                                                                                                                          
+model_input = tokenizer(question, truncation=True, padding=True, return_tensors="pt")
+generated_answers_encoded = model.generate(input_ids=model_input["input_ids"].to(device),attention_mask=model_input["attention_mask"].to(device),
+                                                                                      force_words_ids=None,
+                                                                                      min_length=1,
+                                                                                      max_length=100,
+                                                                                      do_sample=True,
+                                                                                      early_stopping=True,
+                                                                                      num_beams=4,
+                                                                                      temperature=1.0,
+                                                                                      top_k=None,
+                                                                                      top_p=None,
+                                                                                      # eos_token_id=tokenizer.eos_token_id,
+                                                                                      no_repeat_ngram_size=2,
+                                                                                      num_return_sequences=1,
+                                                                                      return_dict_in_generate=True,
+                                                                                      output_scores=True)
+response = tokenizer.batch_decode(generated_answers_encoded['sequences'], skip_special_tokens=True,clean_up_tokenization_spaces=True)    
+print(response)
+
+```
+
+Have fun!