Update README.md

README.md

@@ -1,12 +1,17 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-classification
 ---
 
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
-
+Predicts whether the news article's title is fake or real.
 
 
 ## Model Details
@@ -14,115 +19,132 @@ tags: []
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
+This model's purpose is to classify, whether the information, given in the news article, is true or false. It was trained on 2 datasets, 
+combined and preprocessed. 0 (LABEL_0) stands for false and 1 stands for true.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+- **Developed by:** Ostap Mykhailiv
+- **Model type:** Classification
+- **Language(s) (NLP):** English
+- **License:** apache-2.0
+- **Finetuned from model:** google-bert/bert-base-uncased
 
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+### Model Usage
+This model can be used for whatever reason you need, also a site hosted, based on this model is here: (todo) 
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+As a Bert model, this also has bias. It can't be considered as a somewhat state-of-the-art model, because 
+it was trained on old data (about 2022 and older), so it may not be considered as a reliable fake-news checker
+about military conflicts in Ukraine, Israel, and so on. Please consider, that the names of people in the data were not preprocessed, so
+it might be also biased toward certain names. 
 
 ### Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+To get better overall results, I decided to make a title truncation in training. Though it increased the overall result for both longer and
+shorter text, one should not give less than 6 and more than 12 words for predictions, excluding stopwords. For the preprocess operations look below.
+One can translate news from language into English, though it may not give the expected results. 
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+from transformers import pipeline
+pipe = pipeline("text-classification", model="omykhailiv/bert-fake-news-recognition")
+pipe.predict('Some text')
 
-## Training Details
+It will return something like this: 
+[{'label': 'LABEL_0', 'score': 0.7248537290096283}]
+Where 'LABEL_0' means false and score means the probability of it.
 
 ### Training Data
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
+https://huggingface.co/datasets/GonzaloA/fake_news
+https://github.com/GeorgeMcIntire/fake_real_news_dataset
+
+#### Preprocessing
+Preprocessing was made by using this function:
+import re
+import string
+import spacy
+from nltk.corpus import stopwords
+lem = spacy.load('en_core_web_sm')
+stop_words = set(stopwords.words('english'))
+def testing_data_prep(text):
+    """
+    Args:
+        text (str): The input text string.
+
+    Returns:
+        str: The preprocessed text string, or an empty string if the length
+             does not meet the specified criteria (8 to 12 words).
+    """
+    # Convert text to lowercase for case-insensitive processing
+    text = str(text).lower()
+
+    # Remove HTML tags and their contents (e.g., "<tag>text</tag>")
+    text = re.sub('<.*?>+\w+<.*?>', '', text)
+
+    # Remove punctuation using regular expressions and string escaping
+    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
+
+    # Remove words containing alphanumeric characters followed by digits
+    # (e.g., "model2023", "data10")
+    text = re.sub('\w*\d\w*', '', text)
+
+    # Remove newline characters
+    text = re.sub('\n', '', text)
+
+    # Replace multiple whitespace characters with a single space
+    text = re.sub('\\s+', ' ', text)
+
+    # Lemmatize words (convert them to their base form)
+    text = lem(text)
+    words = [word.lemma_ for word in text]
+    
+    # Removing stopwords, such as do, not, as, etc. (https://gist.github.com/sebleier/554280)
+    new_filtered_words = [
+    word for word in words if word not in stopwords.words('english')]
+    if 12 >= len(new_filtered_words) >= 6:
+      return ' '.join(new_filtered_words)
+    return ' '.join(new_filtered_words)
 
 
 #### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
+The following hyperparameters were used during training:
+
+ - learning_rate: 2e-5
+ - train_batch_size: 32
+ - eval_batch_size: 32
+ - num_epochs: 5
+ - warmup_steps: 500
+ - weight_decay: 0.03
+ - random seed: 42
+   
 #### Speeds, Sizes, Times [optional]
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
 [More Information Needed]
 
-## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
+### Testing Data, Metrics
 
 #### Testing Data
 
 <!-- This should link to a Dataset Card if possible. -->
 
-[More Information Needed]
-
-#### Factors
+https://huggingface.co/datasets/GonzaloA/fake_news
+https://github.com/GeorgeMcIntire/fake_real_news_dataset
+https://onlineacademiccommunity.uvic.ca/isot/2022/11/27/fake-news-detection-datasets/
+https://arxiv.org/pdf/1806.00749v1, the dataset download link: https://drive.google.com/file/d/0B3e3qZpPtccsMFo5bk9Ib3VCc2c/view?resourcekey=0-_eqAfKOCKbuE-xFFCmEzyg
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
 
 #### Metrics
-
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Accuracy
 
 ### Results
 
@@ -131,71 +153,6 @@ Use the code below to get started with the model.
 #### Summary
 
 
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-
-
+Tesla T4 GPU, available for free in Google Collab