Update README.md

README.md

@@ -31,3 +31,56 @@ Please cite the original paper if you decide to use the model:
 
 `awesome-align` is a tool that can extract word alignments from multilingual BERT (mBERT) [Demo](https://colab.research.google.com/drive/1205ubqebM0OsZa1nRgbGJBtitgHqIVv6?usp=sharing) and allows you to fine-tune mBERT on parallel corpora for better alignment quality (see our paper for more details).
 
+## Usage
+
+```python
+from transformers import AutoModel, AutoTokenizer
+import itertools
+import torch
+
+# load model
+model = AutoModel.from_pretrained("aneuraz/awesome-align-with-co")
+tokenizer = AutoTokenizer.from_pretrained("aneuraz/awesome-align-with-co")
+
+# model parameters
+align_layer = 8
+threshold = 1e-3
+
+# define inputs
+src = 'awesome-align is awesome !'
+tgt = '牛对齐 是 牛 ！'
+
+# pre-processing
+sent_src, sent_tgt = src.strip().split(), tgt.strip().split()
+token_src, token_tgt = [tokenizer.tokenize(word) for word in sent_src], [tokenizer.tokenize(word) for word in sent_tgt]
+wid_src, wid_tgt = [tokenizer.convert_tokens_to_ids(x) for x in token_src], [tokenizer.convert_tokens_to_ids(x) for x in token_tgt]
+ids_src, ids_tgt = tokenizer.prepare_for_model(list(itertools.chain(*wid_src)), return_tensors='pt', model_max_length=tokenizer.model_max_length, truncation=True)['input_ids'], tokenizer.prepare_for_model(list(itertools.chain(*wid_tgt)), return_tensors='pt', truncation=True, model_max_length=tokenizer.model_max_length)['input_ids']
+sub2word_map_src = []
+for i, word_list in enumerate(token_src):
+  sub2word_map_src += [i for x in word_list]
+sub2word_map_tgt = []
+for i, word_list in enumerate(token_tgt):
+  sub2word_map_tgt += [i for x in word_list]
+  
+# alignment
+align_layer = 8
+threshold = 1e-3
+model.eval()
+with torch.no_grad():
+  out_src = model(ids_src.unsqueeze(0), output_hidden_states=True)[2][align_layer][0, 1:-1]
+  out_tgt = model(ids_tgt.unsqueeze(0), output_hidden_states=True)[2][align_layer][0, 1:-1]
+
+  dot_prod = torch.matmul(out_src, out_tgt.transpose(-1, -2))
+
+  softmax_srctgt = torch.nn.Softmax(dim=-1)(dot_prod)
+  softmax_tgtsrc = torch.nn.Softmax(dim=-2)(dot_prod)
+
+  softmax_inter = (softmax_srctgt > threshold)*(softmax_tgtsrc > threshold)
+
+align_subwords = torch.nonzero(softmax_inter, as_tuple=False)
+align_words = set()
+for i, j in align_subwords:
+  align_words.add( (sub2word_map_src[i], sub2word_map_tgt[j]) )
+  
+print(align_words
+```