nirusanan
/

tinyBert-keyword

@@ -1,199 +1,136 @@
 ---
 library_name: transformers
-tags: []
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 ---
 library_name: transformers
+language:
+- en
+base_model:
+- huawei-noah/TinyBERT_General_4L_312D
+pipeline_tag: token-classification
 ---
+# Model Description
+Keyphrase extraction is a technique in text analysis where you extract the keyphrases from a paragraph.
+The **tinyBert-keyword** model is a fine-tuned version of the huawei-noah/TinyBERT_General_4L_312D model, tailored specifically for Keyphrase extraction.
+**huawei-noah/TinyBERT_General_4L_312D** is a distilled version of BERT, specifically designed to be smaller and faster for general NLP tasks.
+- **Finetuned from:** huawei-noah/TinyBERT_General_4L_312D
+## How to use
+```python
+import torch
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+```
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+import difflib
+tokenizer = AutoTokenizer.from_pretrained("nirusanan/tinyBert-keyword")
+model = AutoModelForTokenClassification.from_pretrained("nirusanan/tinyBert-keyword").to(device)
+```
+```python
+text = """
+Computer Vision: VLMs are trained on large datasets of images, videos, or other visual data. They use deep neural networks to extract features and represent the visual information.
+Natural Language Processing (NLP): VLMs are also trained on large datasets of text, which enables them to understand and generate natural language.
+Cross-modal Interaction: The combination of computer vision and NLP allows the VLM to interact and process both visual and textual data in a unified manner.
+Types of Vision Language Models:
+Visual-Bert: Visual-BERT (Bilinear Pooling for Visual Question Answering) is a popular VLM that uses a combination of visual feature extractors and language models.
+LXMERT: LXMERT (Large Scale Instance and Instance-Specific Multimodal Representation Learning) is a VLM designed for visual reasoning and question answering tasks.
+VL-BERT: VL-BERT (Visual Large Language Bert) is a VLM that uses a transformer-based architecture to model visual and textual representations.
+"""
+```
+```python
+id2label  = model.config.id2label
+tokenized = tokenizer(
+        text,
+        padding=True,
+        truncation=True,
+        return_offsets_mapping=True,
+        return_tensors="pt"
+    )
+input_ids = tokenized["input_ids"].to(device)
+attention_mask = tokenized["attention_mask"].to(device)
+outputs = model(input_ids=input_ids, attention_mask=attention_mask)
+predictions = torch.argmax(outputs.logits, dim=2)
+tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
+token_predictions = [id2label[pred.item()] for pred in predictions[0]]
+```
+```python
+entities = []
+current_entity = None
+for idx, (token, pred) in enumerate(zip(tokens, token_predictions)):
+    if pred.startswith("B-"):
+        if current_entity:
+            entities.append(current_entity)
+        current_entity = {"type": pred[2:], "start": idx, "text": token}
+    elif pred.startswith("I-") and current_entity:
+            current_entity["text"] += f" {token}"
+    elif current_entity:
+        entities.append(current_entity)
+        current_entity = None
+if current_entity:
+    entities.append(current_entity)
+```
+```python
+keywords = []
+for i in entities:
+    keywords.append(i['text'])
+```
+```python
+def clean_keyword(keyword):
+    return keyword.replace(" ##", "")
+def find_closest_word(keyword, word_positions):
+    keyword_cleaned = clean_keyword(keyword)
+    best_match = None
+    best_score = float('inf')
+    for pos, word in word_positions.items():
+        score = difflib.SequenceMatcher(None, keyword_cleaned, word).ratio()
+        if score > 0.8 and (best_match is None or score > best_score):
+            best_match = word
+            best_score = score
+    return best_match or keyword_cleaned
+```
+```python
+words = text.split()
+word_positions = {i: word.strip(".,") for i, word in enumerate(words)}
+cleaned_keywords = []
+for keyword in keywords:
+    closest_word = find_closest_word(keyword, word_positions)
+    cleaned_keywords.append({'text': closest_word})
+```
+```python
+unique_keywords = {}
+for item in cleaned_keywords:
+    text = item['text'].lower()
+    if text not in unique_keywords:
+        unique_keywords[text] = item
+cleaned_keywords_unique = list(unique_keywords.values())
+if len(cleaned_keywords_unique) > 5:
+  final_keywords = cleaned_keywords_unique[:5]
+else:
+  final_keywords = cleaned_keywords_unique
+text_values = [item['text'] for item in final_keywords]
+text_values
+```