Update README.md

README.md

@@ -9,7 +9,17 @@ datasets:
 metrics:
 - seqeval
 widget:
-- text: "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases 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 NLP, transformers can be used to improve keyphrase extraction. Transformers also focus on the semantics and context of a document, which is quite an improvement."
+- text: "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. 
+Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading 
+it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail 
+and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents, 
+this process can take a lot of time. 
+
+Here is where Artificial Intelligence comes in. Currently, classical machine learning methods, that use statistical 
+and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture 
+the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, 
+occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies 
+and context of words in a text."
   example_title: "Example 1"
 - text: "FoodEx is the largest trade exhibition for food and drinks in Asia, with about 70,000 visitors checking out the products presented by hundreds of participating companies. I was lucky to enter as press; otherwise, visitors must be affiliated with the food industry— and pay ¥5,000 —  to enter. The FoodEx menu is global, including everything from cherry beer from Germany and premium Mexican tequila to top-class French and Chinese dumplings. The event was a rare chance to try out both well-known and exotic foods and even see professionals making them. In addition to booths offering traditional Japanese favorites such as udon and maguro sashimi, there were plenty of innovative twists, such as dorayaki , a sweet snack made of two pancakes and a red-bean filling, that came in coffee and tomato flavors. While I was there I was lucky to catch the World Sushi Cup Japan 2013, where top chefs from around the world were competing … and presenting a wide range of styles that you would not normally see in Japan, like the flower makizushi above."
   example_title: "Example 2"
@@ -23,18 +33,23 @@ model-index:
       type: midas/openkp
       name: openkp
     metrics:
-      - type: seqeval 
+      - type: F1 (Seqeval) 
         value: 0.430
-        name: F1-score
+        name: F1 (Seqeval)
+      - type: F1@M
+        value: 0.314
+        name: F1@M          
 ---
-# 🔑 Keyphrase Extraction model: distilbert-openkp
-Keyphrase extraction is a technique in text analysis where you extract the important keyphrases 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 NLP, transformers can be used to improve keyphrase extraction. Transformers also focus on the semantics and context of a document, which is quite an improvement.
+# 🔑 Keyphrase Extraction Model: distilbert-openkp
+Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents, this process can take a lot of time ⏳. 
+
+Here is where Artificial Intelligence 🤖 comes in. Currently, classical machine learning methods, that use statistical and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies and context of words in a text.
 
 
 ## 📓 Model Description
-This model is a fine-tuned distilbert model on the OpenKP dataset. More information can be found here: https://huggingface.co/distilbert-base-uncased.
+This model uses [KBIR](https://huggingface.co/distilbert-base-uncased) as its base model and fine-tunes it on the [OpenKP dataset](https://huggingface.co/datasets/midas/openkp). 
 
-The model is fine-tuned as a token classification problem where the text is labeled using the BIO scheme.
+Keyphrase extraction models are transformer models fine-tuned as a token classification problem where each word in the document is classified as being part of a keyphrase or not.
 
 | Label | Description                     |
 | ----- | ------------------------------- |
@@ -42,11 +57,11 @@ The model is fine-tuned as a token classification problem where the text is labe
 | I-KEY | Inside a keyphrase              |
 | O     | Outside a keyphrase             |
 
-## ✋ Intended uses & limitations
+## ✋ Intended Uses & Limitations
 ### 🛑 Limitations
 * Limited amount of predicted keyphrases.
 * Only works for English documents.
-* For a custom model, please consult the training notebook for more information (link incoming).
+* For a custom model, please consult the [training notebook]() for more information.
 
 ### ❓ How to use
 ```python
@@ -71,7 +86,7 @@ class KeyphraseExtractionPipeline(TokenClassificationPipeline):
     def postprocess(self, model_outputs):
         results = super().postprocess(
             model_outputs=model_outputs,
-            aggregation_strategy=AggregationStrategy.SIMPLE,
+            aggregation_strategy=AggregationStrategy.FIRST,
         )
         return np.unique([result.get("word").strip() for result in results])
 
@@ -81,41 +96,49 @@ class KeyphraseExtractionPipeline(TokenClassificationPipeline):
 # Load pipeline
 model_name = "ml6team/keyphrase-extraction-distilbert-openkp"
 extractor = KeyphraseExtractionPipeline(model=model_name)
+
 ```
 
 ```python
 # Inference
 text = """
-Keyphrase extraction is a technique in text analysis where you extract the important keyphrases 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 NLP, 
-transformers can be used to improve keyphrase extraction. Transformers also focus on the semantics 
-and context of a document, which is quite an improvement.
-""".replace(
-    "\n", ""
-)
+Keyphrase extraction is a technique in text analysis where you extract the
+important keyphrases from a document. Thanks to these keyphrases humans can
+understand the content of a text very quickly and easily without reading it
+completely. Keyphrase extraction was first done primarily by human annotators,
+who read the text in detail and then wrote down the most important keyphrases.
+The disadvantage is that if you work with a lot of documents, this process
+can take a lot of time. 
+
+Here is where Artificial Intelligence comes in. Currently, classical machine
+learning methods, that use statistical and linguistic features, are widely used
+for the extraction process. Now with deep learning, it is possible to capture
+the semantic meaning of a text even better than these classical methods.
+Classical methods look at the frequency, occurrence and order of words
+in the text, whereas these neural approaches can capture long-term
+semantic dependencies and context of words in a text.
+""".replace("\n", " ")
 
 keyphrases = extractor(text)
 
 print(keyphrases)
+
 ```
 
 ```
 # Output
-['keyphrase extraction', 'text analysis']
+['keyphrase extraction' 'text analysis']
 ```
 
 ## 📚 Training Dataset
-OpenKP is a large-scale, open-domain keyphrase extraction dataset with 148,124 real-world web documents along with 1-3 most relevant human-annotated keyphrases.
+[OpenKP](https://github.com/microsoft/OpenKP) is a large-scale, open-domain keyphrase extraction dataset with 148,124 real-world web documents along with 1-3 most relevant human-annotated keyphrases.
 
-You can find more information here: https://github.com/microsoft/OpenKP
+You can find more information in the [paper](https://arxiv.org/abs/1911.02671). 
 
-## 👷‍♂️ Training procedure
-For more in detail information, you can take a look at the training notebook (link incoming).
+## 👷‍♂️ Training Procedure
+For more in detail information, you can take a look at the [training notebook]().
 
-### Training parameters
+### Training Parameters
 
 | Parameter | Value |
 | --------- | ------|
@@ -125,7 +148,26 @@ For more in detail information, you can take a look at the training notebook (li
 
 ### Preprocessing
 The documents in the dataset are already preprocessed into list of words with the corresponding labels. The only thing that must be done is tokenization and the realignment of the labels so that they correspond with the right subword tokens.
+
 ```python
+from datasets import load_dataset
+from transformers import AutoTokenizer
+
+# Labels
+label_list = ["B", "I", "O"]
+lbl2idx = {"B": 0, "I": 1, "O": 2}
+idx2label = {0: "B", 1: "I", 2: "O"}
+
+# Tokenizer
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+max_length = 512
+
+# Dataset parameters
+dataset_full_name = "midas/openkp"
+dataset_subset = "raw"
+dataset_document_column = "document"
+dataset_biotags_column = "doc_bio_tags"
+
 def preprocess_fuction(all_samples_per_split):
     tokenized_samples = tokenizer.batch_encode_plus(
         all_samples_per_split[dataset_document_column],
@@ -159,10 +201,17 @@ def preprocess_fuction(all_samples_per_split):
         total_adjusted_labels.append(adjusted_label_ids)
     tokenized_samples["labels"] = total_adjusted_labels
     return tokenized_samples
+
+# Load dataset
+dataset = load_dataset(dataset_full_name, dataset_subset)
+
+# Preprocess dataset
+tokenized_dataset = dataset.map(preprocess_fuction, batched=True)
+    
 ```
 
-### Postprocessing
-For the post-processing, you will need to filter out the B and I labeled tokens and concat the consecutive Bs and Is. As last you strip the keyphrase to ensure all spaces are removed.
+### Postprocessing (Without Pipeline Function)
+If you do not use the pipeline function, you must filter out the B and I labeled tokens. Each B and I will then be merged into a keyphrase. Finally, you need to strip the keyphrases to make sure all unnecessary spaces have been removed.
 ```python
 # Define post_process functions
 def concat_tokens_by_tag(keyphrases):
@@ -194,9 +243,9 @@ def extract_keyphrases(example, predictions, tokenizer, index=0):
     return np.unique([kp.strip() for kp in extracted_kps])
 
 ```
-## 📝 Evaluation results
 
-One of the traditional evaluation methods is the precision, recall and F1-score @k,m where k is the number that stands for the first k predicted keyphrases and m for the average amount of predicted keyphrases.
+## 📝 Evaluation Results
+Traditional evaluation methods are the precision, recall and F1-score @k,m where k is the number that stands for the first k predicted keyphrases and m for the average amount of predicted keyphrases.
 The model achieves the following results on the OpenKP test set:
 
 | Dataset           | P@5  | R@5  | F1@5 | P@10 | R@10 | F1@10 | P@M  | R@M  | F1@M |