Update README.md

README.md

@@ -8,7 +8,8 @@ tags:
 license: mit
 language:
 - en
-metrics: null
+metrics:
+- accuracy
 base_model:
 - ProsusAI/finbert
 ---
@@ -16,60 +17,62 @@ base_model:
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
-
+TariffBERT is a fine-tuned version of **ProsusAI/finbert** for **financial sentiment analysis** focused on *tariff and trade-policy news*.  
+It classifies English-language text into **Positive**, **Negative** or **Neutral** sentiment toward tariff-related market impact.
 
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-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]
+- **Developed by:** Cristobal Medina Meza ([@CristobalMe](https://huggingface.co/CristobalMe))
+- **Model type:** BERT-based sequence classifier
+- **Language:** English
+- **License:** MIT
+- **Finetuned from:** [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert)
 
-### Model Sources [optional]
+### Model Sources [optional] TO DO
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+<!-- - **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. -->
+- Sentiment classification of news articles and headlines, regulatory filings, or analyst notes discussing **tariffs, trade wars, or import/export policy**.
+- Can be used as-is via the Hugging Face `pipeline("text-classification")`.
+
 
-[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]
+- As a component in financial forecasting, event-driven trading strategies, or risk dashboards.
+- Further fine-tuning on sector-specific trade data.
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+- Non-financial general sentiment tasks (movie reviews, product opinions).
+- High-stakes decision-making (e.g., compliance enforcement) without human oversight.
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+- Domain bias: Training data is tariff/trade news; performance may degrade on unrelated finance text.
+
+- Temporal drift: Model reflects market language up to its training cutoff SEPTEMBER 2025; newer policy jargon may be misclassified.
+
+- Geographic bias: Data may over-represent US trade discourse.
 
 ### Recommendations
 
@@ -77,13 +80,15 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
-## How to Get Started with the Model
+**Use confidence thresholds and human review in production.**
+
+## How to Get Started with the Model TO DO
 
 Use the code below to get started with the model.
 
 [More Information Needed]
 
-## Training Details
+## Training Details TO DO
 
 ### Training Data
 
@@ -152,13 +157,10 @@ Use the code below to get started with the model.
 
 <!-- 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]
+- **Hardware Type:** Apple MacBook Pro 14″ (M4 Pro, 14-core CPU / 20-core GPU)
+- **Training Time:** ~15 minutes
+- **Energy Use Estimate:** ≈0.02 kWh  
+- **Estimated Carbon Emissions:** ≈0.01 kg CO2eq  
 
 ## Technical Specifications [optional]
 
@@ -206,4 +208,5 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions or collaboration, email [hello@cmm.fyi](hello@cmm.fyi)
+Or contact [@CristobalMe](https://github.com/CristobalMe)