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Mahalingam
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DistilBart-Med-Summary

Summarization
Transformers
PyTorch
English
bart
text2text-generation
sagemaker
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  1. README.md +210 -0
  2. all_results.json +22 -0
  3. config.json +76 -0
  4. eval_results.json +11 -0
  5. merges.txt +0 -0
  6. pytorch_model.bin +3 -0
  7. special_tokens_map.json +1 -0
  8. tokenizer_config.json +1 -0
  9. train_results.json +14 -0
  10. trainer_state.json +23 -0
  11. training_args.bin +3 -0
  12. vocab.json +0 -0
README.md ADDED
@@ -0,0 +1,210 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
2
+ language: en
3
+ license: apache-2.0
4
+ tags:
5
+ - sagemaker
6
+ - bart
7
+ - summarization
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+ datasets:
9
+ - samsum
10
+ widget:
11
+ - text: "Jeff: Can I train a \U0001F917 Transformers model on Amazon SageMaker? \n\
12
+ Philipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff:\
13
+ \ ok.\nJeff: and how can I get started? \nJeff: where can I find documentation?\
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+ \ \nPhilipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face "
15
+ model-index:
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+ - name: philschmid/distilbart-cnn-12-6-samsum
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+ results:
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+ - task:
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+ type: summarization
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+ name: Summarization
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+ dataset:
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+ name: samsum
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+ type: samsum
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+ config: samsum
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+ split: test
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+ metrics:
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+ - type: rouge
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+ - task:
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+ type: summarization
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+ name: Summarization
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+ dataset:
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+ name: cnn_dailymail
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+ type: cnn_dailymail
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+ config: 3.0.0
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+ split: test
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+ metrics:
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135
+ ---
136
+
137
+ ## `distilbart-cnn-12-6-samsum`
138
+
139
+ This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
140
+
141
+ For more information look at:
142
+ - [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html)
143
+ - [Example Notebooks](https://github.com/huggingface/notebooks/tree/master/sagemaker)
144
+ - [Amazon SageMaker documentation for Hugging Face](https://docs.aws.amazon.com/sagemaker/latest/dg/hugging-face.html)
145
+ - [Python SDK SageMaker documentation for Hugging Face](https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/index.html)
146
+ - [Deep Learning Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers)
147
+
148
+ ## Hyperparameters
149
+ ```json
150
+ {
151
+ "dataset_name": "samsum",
152
+ "do_eval": true,
153
+ "do_train": true,
154
+ "fp16": true,
155
+ "learning_rate": 5e-05,
156
+ "model_name_or_path": "sshleifer/distilbart-cnn-12-6",
157
+ "num_train_epochs": 3,
158
+ "output_dir": "/opt/ml/model",
159
+ "per_device_eval_batch_size": 8,
160
+ "per_device_train_batch_size": 8,
161
+ "seed": 7
162
+ }
163
+ ```
164
+
165
+ ## Train results
166
+
167
+ | key | value |
168
+ | --- | ----- |
169
+ | epoch | 3.0 |
170
+ | init_mem_cpu_alloc_delta | 180338 |
171
+ | init_mem_cpu_peaked_delta | 18282 |
172
+ | init_mem_gpu_alloc_delta | 1222242816 |
173
+ | init_mem_gpu_peaked_delta | 0 |
174
+ | train_mem_cpu_alloc_delta | 6971403 |
175
+ | train_mem_cpu_peaked_delta | 640733 |
176
+ | train_mem_gpu_alloc_delta | 4910897664 |
177
+ | train_mem_gpu_peaked_delta | 23331969536 |
178
+ | train_runtime | 155.2034 |
179
+ | train_samples | 14732 |
180
+ | train_samples_per_second | 2.242 |
181
+
182
+ ## Eval results
183
+
184
+ | key | value |
185
+ | --- | ----- |
186
+ | epoch | 3.0 |
187
+ | eval_loss | 1.4209576845169067 |
188
+ | eval_mem_cpu_alloc_delta | 868003 |
189
+ | eval_mem_cpu_peaked_delta | 18250 |
190
+ | eval_mem_gpu_alloc_delta | 0 |
191
+ | eval_mem_gpu_peaked_delta | 328244736 |
192
+ | eval_runtime | 0.6088 |
193
+ | eval_samples | 818 |
194
+ | eval_samples_per_second | 1343.647 |
195
+
196
+
197
+ ## Usage
198
+ ```python
199
+ from transformers import pipeline
200
+ summarizer = pipeline("summarization", model="philschmid/distilbart-cnn-12-6-samsum")
201
+
202
+ conversation = '''Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker?
203
+ Philipp: Sure you can use the new Hugging Face Deep Learning Container.
204
+ Jeff: ok.
205
+ Jeff: and how can I get started?
206
+ Jeff: where can I find documentation?
207
+ Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face
208
+ '''
209
+ nlp(conversation)
210
+ ```
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+ {
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+ "epoch": 3.0,
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+ "eval_samples_per_second": 1343.647,
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+ "init_mem_gpu_alloc_delta": 1222242816,
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+ "init_mem_gpu_peaked_delta": 0,
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+ "train_samples": 14732,
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+ "train_samples_per_second": 2.242
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+ }
config.json ADDED
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+ {
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+ "_name_or_path": "sshleifer/distilbart-cnn-12-6",
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+ "activation_function": "gelu",
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+ "BartForConditionalGeneration"
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+ ],
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+ "gradient_checkpointing": false,
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+ "id2label": {
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+ "0": "LABEL_0",
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+ "1": "LABEL_1",
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+ "2": "LABEL_2"
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+ },
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+ "model_type": "bart",
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+ "num_beams": 4,
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+ "output_past": true,
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+ "min_length": 56,
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+ "num_beams": 4
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+ }
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+ },
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+ "transformers_version": "4.4.2",
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+ "use_cache": true,
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