Update README.md

README.md

@@ -23,23 +23,16 @@ Our fine-tuned FinBERT model is a powerful tool designed for sentiment analysis
 
 <!-- Provide a longer summary of what this model is. -->
 
-
-
 - **Developed by:** Khushi Dave
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** English
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** yiyanghkust/finbert-tone
+- **Model type:** BERT (Bidirectional Encoder Representations from Transformers)
+- **Language:** English
+- **Finetuned from model:** yiyanghkust/finbert-tone
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
 - **Repository:** https://huggingface.co/kdave/FineTuned_Finbert
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
@@ -73,28 +66,23 @@ results = nlp_pipeline(sentences)
 print(results)
 ```
 
-[More Information Needed]
-
-### 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
 
 1. Misuse:
+
 Deliberate Misinformation: The model may be misused if fed with intentionally crafted misinformation to manipulate sentiment analysis results. Users should ensure the input data is authentic and unbiased.
 
 2. Malicious Use:
+
 Market Manipulation Attempts: Any attempt to use the model to propagate false sentiment for the purpose of market manipulation is strictly unethical and against the intended use of the model.
 
 3. Limitations:
+
 Non-Financial Texts: The model is fine-tuned specifically for Indian stock market news. It may not perform optimally when applied to non-financial texts or unrelated domains.
+
 Extreme Outliers: Unusual or extreme cases in sentiment expression might pose challenges. The model's performance might be less reliable for exceptionally rare or unconventional sentiment expressions.
-Non-Standard Language: The model's training data primarily comprises standard financial language. It may not perform as well when faced with non-standard language, colloquialisms, or slang.
 
-[More Information Needed]
+Non-Standard Language: The model's training data primarily comprises standard financial language. It may not perform as well when faced with non-standard language, colloquialisms, or slang.
 
 ## Bias, Risks, and Limitations
 
@@ -129,7 +117,6 @@ Non-Standard Language: The model's training data primarily comprises standard fi
 
 Understanding these limitations, users are advised to interpret model outputs judiciously, considering the context and potential biases. Transparent communication and awareness of both technical and sociotechnical constraints are essential for responsible model usage. While the model is a valuable tool, it is not infallible, and decision-makers should exercise prudence and diligence.
 
-[More Information Needed]
 
 ### Recommendations
 
@@ -186,12 +173,8 @@ Integrate this model seamlessly into your financial NLP research or analysis wor
 
 Now, you're all set to harness the power of the Fine-Tuned FinBERT model. Happy analyzing! 📈🚀
 
-[More Information Needed]
-
 ## Training Details
 
-### Training Data
-
 **Dataset Information:**
 
 The Fine-Tuned FinBERT model was trained on a carefully curated dataset consisting of Indian financial news articles with summaries. Here's a brief overview of the dataset and its preparation:
@@ -211,125 +194,4 @@ The Fine-Tuned FinBERT model was trained on a carefully curated dataset consisti
 **Dataset Card:**
 For more detailed information on the dataset, including statistics, features, and documentation related to data pre-processing, please refer to the associated [Dataset Card](link-to-dataset-card).
 
-This meticulous curation and diverse data incorporation contribute to the model's proficiency in capturing nuanced sentiment expressions relevant to the Indian stock market.
-
-[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]
-
-
-#### 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 -->
-
-#### 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
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- 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]
-
-### Results
-
-[More Information Needed]
-
-#### 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]
-
-
+This meticulous curation and diverse data incorporation contribute to the model's proficiency in capturing nuanced sentiment expressions relevant to the Indian stock market.