Update README.md

README.md

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 # mBART-large-50 fine-tuned onpus100 and opusbook for English to Portuguese translation.
-[mBART-50](https://huggingface.co/facebook/mbart-large-50/) large fine-tuned on [opus100](https://huggingface.co/datasets/viewer/?dataset=opus100) and [opusbooks](https://huggingface.co/datasets/viewer/?dataset=opusbooks) datasets for **NMT** downstream task.
+[mBART-50](https://huggingface.co/facebook/mbart-large-50/) large fine-tuned on [opus100](https://huggingface.co/datasets/viewer/?dataset=opus100) dataset for **NMT** downstream task.
 
 # Details of mBART-50 🧠
 
@@ -29,38 +29,36 @@ where spans of text are replaced with a single mask token. The model is then tas
 The decoder input is the original text with one position offset. A language id symbol `LID` is used as the initial token to predict the sentence.
 
 
-## Details of the downstream task (Sequence Classification as Text generation) - Dataset 📚
+## Details of the downstream task (NMT) - Dataset 📚
 
-[tweets_hate_speech_detection](hhttps://huggingface.co/datasets/tweets_hate_speech_detection)
+- **Homepage:** [Link](http://opus.nlpl.eu/opus-100.php) 
+- **Repository:** [GitHub](https://github.com/EdinburghNLP/opus-100-corpus)
+- **Paper:** [ARXIV](https://arxiv.org/abs/2004.11867)
 
+### Dataset Summary
 
-The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.
+OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side. The corpus covers 100 languages (including English). Selected the languages based on the volume of parallel data available in OPUS.
 
-Formally, given a training sample of tweets and labels, where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist, your objective is to predict the labels on the given test dataset.
 
-- Data Instances:
+### Languages
 
-The dataset contains a label denoting is the tweet a hate speech or not
+OPUS-100 contains approximately 55M sentence pairs. Of the 99 language pairs, 44 have 1M sentence pairs of training data, 73 have at least 100k, and 95 have at least 10k.
 
-```json
-{'label': 0,  # not a hate speech
- 'tweet': ' @user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction.   #run'}
-```
-- Data Fields:
- 
-**label**: 1 - it is a hate speech, 0 - not a hate speech
+## Dataset Structure
 
-**tweet**: content of the tweet as a string
 
-- Data Splits:
+### Data Fields
 
-The data contains training data with **31962** entries
+- `src_tag`: `string` text in source language
+- `tgt_tag`: `string` translation of source language in target language
 
-## Test set metrics 🧾
+### Data Splits
 
-We created a representative test set with the 5% of the entries.
+The dataset is split into training, development, and test portions. Data was prepared by randomly sampled up to 1M sentence pairs per language pair for training and up to 2000 each for development and test. To ensure that there was no overlap (at the monolingual sentence level) between the training and development/test data, they applied a filter during sampling to exclude sentences that had already been sampled. Note that this was done cross-lingually so that, for instance, an English sentence in the Portuguese-English portion of the training data could not occur in the Hindi-English test set.
+
+## Test set metrics 🧾
 
-The dataset is so imbalanced and we got a **F1 score of 79.8**
+We got a **BLEU score of 20.61**