Upload acta anonymizer adapter - Latest (v20250914_034323)

README.md

@@ -1,91 +1,206 @@
 ---
-license: apache-2.0
-language:
-- ro
 base_model: EvanD/xlm-roberta-base-romanian-ner-ronec
+library_name: peft
 tags:
-- token-classification
-- named-entity-recognition
-- pii-detection
-- romanian
-- moldova
-- financial-pii
-- banking
-- fintech
+- base_model:adapter:EvanD/xlm-roberta-base-romanian-ner-ronec
+- lora
+- transformers
 ---
 
-# Finguys/acta-anonymizer-financial
+# Model Card for Model ID
 
-Acta Anonymizer Financial Adapter
+<!-- Provide a quick summary of what the model is/does. -->
 
-This model is a fine-tuned adapter for Romanian financial text anonymization. 
-It's based on XLM-RoBERTa and trained specifically for detecting and anonymizing 
-PII in Romanian financial documents from Moldova.
 
-Key features:
-- Romanian language support
-- Financial domain specialization
-- GDPR compliance focused
-- High accuracy PII detection
 
-Use cases:
-- Banking document anonymization
-- Financial report processing
-- Compliance data handling
+## Model Details
 
+### Model Description
 
-**Current Version**: 20250914_053826
+<!-- Provide a longer summary of what this model is. -->
 
-## Key Features
 
-- Romanian language support
-- GDPR compliance focused
-- High accuracy PII detection
-- Domain-specific fine-tuning
 
-## Use Cases
+- **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]
 
-- Banking document anonymization
-- Financial report processing
-- Compliance data handling
+### Model Sources [optional]
 
-## Training Data
+<!-- Provide the basic links for the model. -->
 
-This model was trained on synthetic Moldovan PII data for financial domain anonymization.
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-## Usage
+## Uses
 
-```python
-from peft import PeftModel
-from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-# Load base model
-model = AutoModelForTokenClassification.from_pretrained("EvanD/xlm-roberta-base-romanian-ner-ronec")
-tokenizer = AutoTokenizer.from_pretrained("EvanD/xlm-roberta-base-romanian-ner-ronec")
+### Direct Use
 
-# Load adapter
-model = PeftModel.from_pretrained(model, "Finguys/acta-anonymizer-financial")
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-# Create pipeline
-ner_pipeline = pipeline(
-    "token-classification",
-    model=model,
-    tokenizer=tokenizer,
-    aggregation_strategy="simple"
-)
+[More Information Needed]
 
-# Example usage
-text = "Ion Popescu are un cont la Banca Transilvania cu IBAN RO49AAAA1B310075938400000."
-entities = ner_pipeline(text)
-print(entities)
-```
+### Downstream Use [optional]
 
-## Training
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-This model was trained using LoRA (Low-Rank Adaptation) on synthetic Moldovan PII data.
+[More Information Needed]
 
-## Versions
+### Out-of-Scope Use
 
-- **Latest**: Root level contains the most recent version
-- **Archived**: Previous versions are stored in `versions/` folder
-- **Version Index**: See `version_history.yaml` for complete version history
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### 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.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### 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]
+
+
+#### 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]
+### Framework versions
+
+- PEFT 0.17.1

adapter_config.json

@@ -29,9 +29,9 @@
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checkpoint-3/adapter_config.json

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