Training update: 6,737/238,451 rows (2.83%) | +26 new @ 2025-10-23 02:35:06

README.md

@@ -9,60 +9,53 @@ tags:
 - cybersecurity
 - fill-mask
 - named-entity-recognition
+- transformers
+- tensorflow
+- pytorch
+- masked-language-modeling
 base_model: boltuix/bert-micro
 library_name: transformers
+pipeline_tag: fill-mask
 ---
 # bert-micro-cybersecurity
-
 ## 1. Model Details
 **Model description**  
 "bert-micro-cybersecurity" is a compact transformer model 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 **2.78%** of planned data.
-
+- Status: **Early version** — trained on **2.83%** of planned data.
 **Model sources**  
 - Base model: [boltuix/bert-micro](https://huggingface.co/boltuix/bert-micro)
 - Data: Cybersecurity Data
-
 ## 2. Uses
 ### 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.
-
 ### 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).
-
 ### Out-of-scope use  
 - Not meant for high-stakes automated blocking decisions without human review.  
 - Not optimized for languages other than English and Indonesian.  
 - Not tested for non-cybersecurity domains or out-of-distribution data.
-
 ## 3. Bias, Risks, and Limitations 
-Because the model is based on a small subset (2.78%) of planned data, performance is preliminary and may degrade on unseen or specialized domains (industrial control, IoT logs, foreign language).
-
+Because the model is based on a small subset (2.83%) 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.
 - Should not be used as sole authority for incident decisions; only as an aid to human analysts.
-
 ## 4. How to Get Started with the Model  
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
 tokenizer = AutoTokenizer.from_pretrained("codechrl/bert-micro-cybersecurity")
 model = AutoModelForSequenceClassification.from_pretrained("codechrl/bert-micro-cybersecurity")
-
 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()
 ```
-
 ## 5. Training Details
-- **Trained records**: 6,637 / 238,430 (2.78%)
+- **Trained records**: 6,737 / 238,451 (2.83%)
 - **Learning rate**: 5e-05
 - **Epochs**: 3
 - **Batch size**: 16

config.json

@@ -17,7 +17,7 @@
   "num_hidden_layers": 2,
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
-  "transformers_version": "4.57.1",
+  "transformers_version": "4.57.0",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 30522

model.safetensors

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training_args.bin

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training_metadata.json

@@ -1,11 +1,11 @@
 {
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-  "trained_at_readable": "2025-10-23 01:52:36",
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-  "new_rows_this_session": 100,
-  "trained_rows_total": 6637,
-  "total_db_rows": 238430,
-  "percentage": 2.783626221532525,
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+  "trained_at_readable": "2025-10-23 02:35:06",
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   "final_loss": 0,
   "epochs": 3,
   "learning_rate": 5e-05