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README.md

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----
-language:
-- en
-tags:
-- summarization
-thumbnail: https://huggingface.co/front/thumbnails/facebook.png
-datasets:
-- samsum
-model-index:
-- name: shauryakudiyal/fine-tuned-bart
-  results:
-  - task:
-      type: summarization
-      name: Summarization
-    dataset:
-      name: samsum
-      type: samsum
-      config: 3.0.0
-      split: train
-    metrics:
-    - name: ROUGE-1
-      type: rouge
-      value: 42.437500
-      verified: true
-    - name: ROUGE-2
-      type: rouge
-      value: 18.446100
-      verified: true
-    - name: ROUGE-L
-      type: rouge
-      value: 32.710300
-      verified: true
-    - name: ROUGE-LSUM
-      type: rouge
-      value: 32.710300
-      verified: true
-    - name: loss
-      type: loss
-      value: 0.606930
-      verified: true
-    - name: gen_len
-      type: gen_len
-      value: 30.200000
-      verified: true
----
-# BART (large-sized model), fine-tuned on SAMSUM 
-
-
-
-## Model description
-
-BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
-
-BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs.
-
-## Intended uses & limitations
-
-You can use this model for text summarization. 
-