Update README.md

README.md

@@ -1,6 +1,102 @@
 ---
+
 language: en
 license: mit
 datasets:
 - midas/inspec
+tags:
+- keyphrase-extraction
+metric:
+- f1
+
 ---
+** Work in progress **
+# 🔑 Keyphrase Extraction model: KBIR-inspec
+Keyword extraction is a technique in text analysis where you extract the important keywords from a text. Since this is a time-consuming process, Artificial Intelligence is used to automate it.
+Currently, classical machine learning methods, that use statistics and linguistics, are widely used for the extraction process. The fact that these methods have been widely used in the community has the advantage that there are many easy-to-use libraries. 
+Now with the recent innovations in deep learning methods (such as recurrent neural networks and transformers, GANS, …), keyword extraction can be improved. These new methods also focus on the semantics and context of a document, which is quite an improvement.
+
+
+## 📓 Model Description
+KBIR pre-trained model fine-tuned on the Inspec dataset. KBIR
+Keyphrase Boundary Infilling with Replacement (KBIR) which utilizes a multi-task learning setup for optimizing a combined loss of Masked Language Modeling (MLM), Keyphrase Boundary Infilling (KBI) and Keyphrase Replacement Classification (KRC).
+Paper: https://arxiv.org/abs/2112.08547
+
+## ✋ Intended uses & limitations
+### ❓ How to use
+```python
+# Define post_process functions
+def concat_tokens_by_tag(keywords):
+    keyphrase_tokens = []
+    for id, label in keywords:
+        if label == "B":
+            keyphrase_tokens.append([id])
+        elif label == "I":
+            if len(keyphrase_tokens) > 0:
+                keyphrase_tokens[len(keyphrase_tokens) - 1].append(id)
+    return keyphrase_tokens
+
+
+def extract_keyphrases(example, predictions, tokenizer, index=0):
+    keyphrases_list = [
+        (id, idx2label[label])
+        for id, label in zip(
+            np.array(example["input_ids"]).squeeze().tolist(), predictions[index]
+        )
+        if idx2label[label] in ["B", "I"]
+    ]
+
+    processed_keyphrases = concat_tokens_by_tag(keyphrases_list)
+    extracted_kps = tokenizer.batch_decode(
+        processed_keyphrases,
+        skip_special_tokens=True,
+        clean_up_tokenization_spaces=True,
+    )
+    return np.unique([kp.strip() for kp in extracted_kps])
+
+# Load model and tokenizer
+model_name = "DeDeckerThomas/keyphrase-extraction-kbir-inspec"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+
+# Inference
+text = """
+""".replace(
+    "\n", ""
+)
+
+encoded_input = tokenizer(
+    text.split(" "),
+    is_split_into_words=True,
+    truncation=True,
+    padding="max_length",
+    max_length=max_length,
+    return_tensors="pt",
+)
+
+output = model(**encoded_input)
+logits = output.logits.detach().numpy()
+predictions = np.argmax(logits, axis=2)
+
+extracted_kps = extract_keyphrases(encoded_input, predictions, tokenizer)
+
+print("***** Input Document *****")
+print(text)
+
+print("***** Prediction *****")
+print(extracted_kps)
+```
+
+## 📚 Trainig Dataset
+## 👷‍♂️ Training procedure
+
+### Preprocessing
+## 📝Evaluation results
+
+The model achieves the following results on the Inspec test set:
+
+| Dataset           | P@5  | R@5  | F1@5 | P@10 | R@10 | F1@10 | P@M  | R@M  | F1@M |
+|:-----------------:|:----:|:----:|:----:|:----:|:----:|:-----:|:----:|:----:|:----:|
+| Inspec Test Set   | 0.53 | 0.47 | 0.46 | 0.36 | 0.58 | 0.41  | 0.58 | 0.60 | 0.56 |
+
+### BibTeX entry and citation info