Add MT inference code to the main README

README.md

@@ -123,8 +123,58 @@ To download and process YouTube-Commons, please refer to the
 [dedicated YouTube-Commons README](https://huggingface.co/datasets/FBK-MT/fama-data/blob/main/scripts/YouTube-Commons-README.md).
 
 The code used to produce all translations with [MADALAD-400 3B-MT](https://huggingface.co/google/madlad400-3b-mt) is the following:
+```python
+import os
+import sys
+import torch
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+modelname = "google/madlad400-3b-mt"
+batch_size = {$BATCH_SIZE}   
+tlang = {$LANGUAGE}
+
+class BatchedMT:
+    def __init__(self, tokenizer, model):
+        self.buffer_lines = []
+        self.model = model
+        if torch.cuda.is_available():
+            self.model = self.model.cuda()
+        self.tokenizer = tokenizer
+
+    def process_line(self, line):
+        self.buffer_lines.append(line.strip())
+        if len(self.buffer_lines) >= BATCHSIZE:
+            self.print_translations()
+            self.buffer_lines = []
+
+    def print_translations(self):
+        outs = self._do_translate()
+        for s in outs:
+            print(s)
+
+    def _do_translate(self):
+        tokens = self.tokenizer(self.buffer_lines, return_tensors="pt", padding=True)
+        if torch.cuda.is_available():
+            tokens = {k: v.cuda() for k, v in tokens.items()}
+        translated = self.model.generate(**tokens, max_new_tokens=512)
+        return [self.tokenizer.decode(t, skip_special_tokens=True) for t in translated]
+
+    def close(self):
+        if len(self.buffer_lines) > 0:
+            self.print_translations()
+            self.buffer_lines = []
+
+
+mt = BatchedMT(
+    AutoTokenizer.from_pretrained(modelname),
+    AutoModelForSeq2SeqLM.from_pretrained(modelname))
+
+for input_line in sys.stdin:
+    mt.process_line("<2" + tlang + "> " + input_line)
+mt.close()
 ```
-```
+where the input text is passad as stdin, `{$BATCH_SIZE}` is the batch size supported on your machine 
+and `{$LANGUAGE}` is either `en` for Italian to English translation and `it` for English to Italian translation.
 
 The script used for filtering the ST datasets is 
 [`filter_tsv_based_on_ratio`](https://huggingface.co/datasets/FBK-MT/fama-data/blob/main/scripts/filter_tsv_based_on_ratio.py) and