Model save

README.md

@@ -1,55 +1,53 @@
 ---
-language:
-- en
-- id
+library_name: transformers
+base_model: codechrl/bert-micro-cybersecurity
 tags:
-- text-classification
-- cybersecurity
-base_model: boltuix/bert-micro
+- generated_from_trainer
+model-index:
+- name: bert-micro-cybersecurity
+  results: []
 ---
 
-# Model Card for “bert-micro-cybersecurity”
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-## 1. Model Details
+# bert-micro-cybersecurity
 
-**Model description**  
-“bert-micro-cybersecurity” is a compact transformer model derived from `boltuix/bert-micro`, adapted for cybersecurity text classification tasks (e.g., threat detection, incident reports, malicious vs benign content).   
-- Model type: fine-tuned lightweight BERT variant  
-- Languages: English & Indonesia
-- Finetuned from: `boltuix/bert-micro`
-- Status: **Early version** — trained on ~ **2%** of planned data.
+This model is a fine-tuned version of [codechrl/bert-micro-cybersecurity](https://huggingface.co/codechrl/bert-micro-cybersecurity) on the None dataset.
 
-**Model sources**  
-- Base model: [boltuix/bert-micro](https://huggingface.co/boltuix/bert-micro) :contentReference[oaicite:3]{index=3}  
-- Data: Cybersecurity Data
+## Model description
 
-## 2. Uses
+More information needed
 
-### Direct use  
-You can use this model to classify cybersecurity-related text — for example, whether a given message, report or log entry indicates malicious intent, abnormal behaviour, or threat presence.
+## Intended uses & limitations
 
-### Downstream use  
-- Embedding extraction for clustering or anomaly detection in security logs.  
-- As part of a pipeline for phishing detection, malicious email filtering, incident triage.  
-- As a feature extractor feeding a downstream system (e.g., alert-generation, SOC dashboard).
+More information needed
 
-### Out-of-scope use  
-- Not meant for high-stakes automated blocking decisions without human review.  
-- Not optimized for languages other than English.  
-- Not tested for non-cybersecurity domains or out-of-distribution data.
+## Training and evaluation data
 
-## 3. Bias, Risks, and Limitations 
-Because the model is based on a very small subset (~ 2%) of planned data, performance is preliminary and may degrade on unseen or specialized domains (industrial control, IoT logs, foreign language).  
-- Inherits any biases present in the base model (`boltuix/bert-micro`) and in the fine-tuning data — e.g., over-representation of certain threat types, vendor or tooling-specific vocabulary. :contentReference[oaicite:4]{index=4}  
-- Should not be used as sole authority for incident decisions; only as an aid to human analysts.
+More information needed
 
-## 4. How to Get Started with the Model  
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-tokenizer = AutoTokenizer.from_pretrained("your-username/bert-micro-cybersecurity")
-model = AutoModelForSequenceClassification.from_pretrained("your-username/bert-micro-cybersecurity")
+## Training procedure
 
-inputs = tokenizer("The server logged an unusual outbound connection to 123.123.123.123", return_tensors="pt", truncation=True, padding=True)
-outputs = model(**inputs)
-logits = outputs.logits
-predicted_class = logits.argmax(dim=-1).item()
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.06
+- num_epochs: 3
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.57.0
+- Pytorch 2.8.0+cu128
+- Datasets 4.2.0
+- Tokenizers 0.22.1

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