Add README

README.md

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+---
+language: en
+tags:
+- bart
+- seq2seq
+- summarization
+license: apache-2.0
+datasets:
+- samsum
+widget:
+- text: | 
+    Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? 
+    Philipp: Sure you can use the new Hugging Face Deep Learning Container. 
+    Jeff: ok.
+    Jeff: and how can I get started? 
+    Jeff: where can I find documentation? 
+    Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face
+model-index:
+- name: bart-base-samsum
+  results:
+  - task: 
+      name: Abstractive Text Summarization
+      type: abstractive-text-summarization
+    dataset:
+      name: "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization" 
+      type: samsum
+    metrics:
+       - name: Validation ROGUE-1
+         type: rogue-1
+         value: 46.6619
+       - name: Validation ROGUE-2
+         type: rogue-2
+         value: 23.3285
+       - name: Validation ROGUE-L
+         type: rogue-l
+         value: 39.4811
+       - name: Test ROGUE-1
+         type: rogue-1
+         value: 44.9932
+       - name: Test ROGUE-2
+         type: rogue-2
+         value: 21.7286
+       - name: Test ROGUE-L
+         type: rogue-l
+         value: 38.1921 
+---
+## `bart-base-samsum`
+This model was obtained by fine-tuning `facebook/bart-base` on Samsum dataset.
+## Hyperparameters
+```json
+{
+    "dataset_name": "samsum",
+    "do_eval": true,
+    "do_predict": true,
+    "do_train": true,
+    "fp16": true,
+    "learning_rate": 2e-05,
+    "model_name_or_path": "facebook/bart-base",
+    "num_train_epochs": 1,
+    "output_dir": "/opt/ml/model",
+    "per_device_eval_batch_size": 4,
+    "per_device_train_batch_size": 4,
+    "predict_with_generate": true,
+    "gradient_accumulation_steps": 2,
+    "weight_decay": 0.01,
+}
+```
+## Usage
+```python
+from transformers import pipeline
+summarizer = pipeline("summarization", model="lidiya/bart-base-samsum")
+conversation = '''Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? 
+Philipp: Sure you can use the new Hugging Face Deep Learning Container. 
+Jeff: ok.
+Jeff: and how can I get started? 
+Jeff: where can I find documentation? 
+Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face                                           
+'''
+nlp(conversation)
+```
+## Results
+| key | value |
+| --- | ----- |
+| eval_rouge1 | 46.6619 |
+| eval_rouge2 | 23.3285 |
+| eval_rougeL | 39.4811 |
+| eval_rougeLsum | 43.0482 |
+| test_rouge1 | 44.9932 |
+| test_rouge2 | 21.7286 |
+| test_rougeL | 38.1921 |
+| test_rougeLsum | 41.2672 |