init

README.md

@@ -1,8 +1,8 @@
 ---
 title: AlephBERT
-emoji: 📊
-colorFrom: indigo
-colorTo: indigo
+emoji: 🥙
+colorFrom: pink
+colorTo: pink
 sdk: streamlit
 app_file: app.py
 pinned: false

app.py

@@ -0,0 +1,192 @@
+import streamlit as st
+from transformers import pipeline
+from transformers.tokenization_utils import TruncationStrategy
+
+import tokenizers
+import pandas as pd
+import requests
+
+st.set_page_config(
+     page_title='AlephBERT Demo',
+     page_icon="🥙",
+     initial_sidebar_state="expanded",
+)
+
+# st.markdown(
+#     """
+# <style>
+
+#     .sidebar .sidebar-content {
+#         background-image: linear-gradient(#3377ff,  #80aaff);
+#     }
+
+#     footer {
+#         color:white;
+#         visibility: hidden;
+#     }
+#     input {
+#         direction: rtl;
+#     }
+#     .stTextInput .instructions {
+#         color: grey;
+#         font-size: 9px;}
+
+# </style>
+# <div style="color:white; font-size:13px; font-family:monospace;position: fixed; z-index: 1; bottom: 0; right:0; background-color: #f63766;margin:3px;padding:8px;border-radius: 5px;"><a href="https://huggingface.co/onlplab/alephbert-base"  target="_blank" style="text-decoration: none;color: white;">Use aleph-bert in your project </a></div>
+# """,
+#     unsafe_allow_html=True,
+# )
+
+models = {
+    "AlephBERT-base": {
+        "name_or_path":"onlplab/alephbert-base",
+        "description":"AlephBERT base model",
+    },
+    "HeBERT-base-TAU": {
+        "name_or_path":"avichr/heBERT",
+        "description":"HeBERT model created by TAU"
+    },
+    "mBERT-base-multilingual-cased": {
+        "name_or_path":"bert-base-multilingual-cased",
+        "description":"Multilingual BERT model"
+    }
+}
+
+@st.cache(show_spinner=False)
+def get_json_from_url(url):
+    return models
+    return requests.get(url).json()
+
+# models = get_json_from_url('https://huggingface.co/spaces/biu-nlp/AlephBERT/raw/main/models.json')
+
+
+
+@st.cache(show_spinner=False, hash_funcs={tokenizers.Tokenizer: str})
+def load_model(model):
+    pipe = pipeline('fill-mask', models[model]['name_or_path'])
+    def do_tokenize(inputs):
+        return pipe.tokenizer(
+                inputs,
+                add_special_tokens=True,
+                return_tensors=pipe.framework,
+                padding=True,
+                truncation=TruncationStrategy.DO_NOT_TRUNCATE,
+            )
+
+    def _parse_and_tokenize(
+        inputs, tokenized=False, **kwargs
+    ):
+        if not tokenized:
+            inputs = do_tokenize(inputs)
+        return inputs
+
+    pipe._parse_and_tokenize = _parse_and_tokenize
+    
+    return pipe, do_tokenize
+
+
+
+
+
+st.title('AlephBERT🥙')
+st.sidebar.markdown(
+    """<div><a  target="_blank" href="https://nlp.biu.ac.il/~rtsarfaty/onlp#"><img src="https://nlp.biu.ac.il/~rtsarfaty/static/landing_static/img/onlp_logo.png"  style="filter: invert(100%);display: block;margin-left: auto;margin-right: auto;
+  width: 70%;"></a>
+      <p style="color:white; font-size:13px; font-family:monospace; text-align: center">AlephBERT Demo &bull; <a href="https://nlp.biu.ac.il/~rtsarfaty/onlp#" style="text-decoration: none;color: white;"  target="_blank">ONLP Lab</a></p></div>
+      <br>""",
+    unsafe_allow_html=True,
+)
+
+mode = 'Models'
+
+if mode == 'Models':
+    model = st.sidebar.selectbox(
+     'Select Model',
+     list(models.keys()))
+    masking_level = st.sidebar.selectbox('Masking Level:', ['Tokens', 'SubWords'])
+    n_res = st.sidebar.number_input(
+        'Number Of Results',
+        format='%d',
+        value=5,
+        min_value=1,
+        max_value=100)
+    
+    model_tags = model.split('-')
+    model_tags[0] = 'Model:' + model_tags[0] 
+
+    st.markdown(''.join([f'<span style="color:white; font-size:13px; font-family:monospace; background-color: #f63766;margin:3px;padding:8px;border-radius: 5px;">{tag}</span>' for tag in model_tags]),unsafe_allow_html=True)
+    st.markdown('___')
+    ####
+    #prepare the model
+    ####
+    
+    unmasker, tokenize = load_model(model)
+    
+    
+    ####
+    # get inputs
+    ####
+            
+    input_text = st.text_input('Insert text you want to mask', '')
+    if input_text:
+        input_masked = None
+        tokenized = tokenize(input_text)
+        ids = tokenized['input_ids'].tolist()[0]
+        subwords = unmasker.tokenizer.convert_ids_to_tokens(ids)
+        
+        if masking_level == 'Tokens':
+            tokens = str(input_text).split()
+            masked_token = st.selectbox('Select token to mask:', [''] + tokens)
+            if masked_token != '':
+                input_masked = ' '.join(token if token != masked_token else '[MASK]' for token in tokens)
+                display_input = input_masked
+        if masking_level == 'SubWords':
+            tokens = subwords
+            idx = st.selectbox('Select token to mask:', list(range(0,len(tokens)-1)), format_func=lambda i: tokens[i] if i else '')
+            tokenized['input_ids'][0][idx] = unmasker.tokenizer.mask_token_id
+            ids = tokenized['input_ids'].tolist()[0]
+            display_input = ' '.join(unmasker.tokenizer.convert_ids_to_tokens(ids[1:-1]))
+            if idx:
+                input_masked = tokenized
+                
+        if input_masked: 
+            st.markdown('#### Input:')
+            ids = tokenized['input_ids'].tolist()[0]
+            subwords = unmasker.tokenizer.convert_ids_to_tokens(ids)
+            st.markdown(f'<p dir="rtl">{display_input}</p>',
+                        unsafe_allow_html=True,
+            )
+            st.markdown('#### Outputs:')
+            res = unmasker(input_masked, tokenized=masking_level == 'SubWords', top_k=n_res)
+            if res:
+                res = [{'Prediction':r['token_str'], 'Completed Sentence':r['sequence'].replace('[SEP]', '').replace('[CLS]', ''), 'Score':r['score']} for r in res]
+                res_table = pd.DataFrame(res)
+                st.table(res_table)
+            
+            
+        
+#         cols = st.beta_columns(len(tokens))
+#         genre = st.radio(
+#      'Select token to mask:', tokens)
+#         for col, token in zip(cols, reversed(tokens)):
+#             col.text(token)
+        
+#         st.text(tokens)
+#         res = unmasker(input_text)
+#         res_table = pd.DataFrame(res)
+#         st.table(res_table)
+#         st.text(res)
+        
+
+
+
+
+
+
+
+
+    
+
+
+
+

requirements.txt

@@ -0,0 +1,5 @@
+torch
+sentencepiece
+transformers==4.4.2
+tokenizers
+pandas