language:
- en
license: mit
library_name: transformers
tags:
- bert
- text-classification
- nlp
- test
model_name: Dummy BERT for Testing
model_id: test/bert-dummy
inference: true
Dummy BERT Model
This is a test model created for experimental upload testing to Hugging Face using dmf-ng.
Model Details
Model Description
A minimal BERT model for testing artifact upload workflows with dmf-ng to Hugging Face Hub.
- Developed by: dmf-ng Test Suite
- Model type: Transformer-based language model
- Library: Transformers
- License: MIT
Model Architecture
- Architecture: BERT (Bidirectional Encoder Representations from Transformers)
- Hidden Size: 768
- Number of Hidden Layers: 12
- Number of Attention Heads: 12
- Intermediate Size: 3,072
- Maximum Position Embeddings: 512
- Vocabulary Size: 30,522
Model Configuration
{
"model_type": "bert",
"hidden_size": 768,
"num_hidden_layers": 12,
"num_attention_heads": 12,
"intermediate_size": 3072,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"attention_probs_dropout_prob": 0.1,
"max_position_embeddings": 512,
"type_vocab_size": 2,
"initializer_range": 0.02,
"layer_norm_eps": 1e-12,
"pad_token_id": 0
}
Files
- model.pt - PyTorch model weights (placeholder)
- config.json - Model configuration in HuggingFace format
- tokenizer.json - Tokenizer configuration
- vocab.txt - Vocabulary file with token mappings
- README.md - This model card
Intended Use
This model is for testing purposes only and should not be used for actual inference or production workloads.
Primary Intended Use
- Testing artifact upload workflows with dmf-ng
- Validating model card metadata
- Experimenting with Hugging Face Hub integration
- Testing lineage tracking with MLflow
Out-of-Scope Use Cases
- Production inference
- Real-world text classification tasks
- Fine-tuning on real datasets
- Deploying to inference endpoints
Technical Details
Model Inputs
- input_ids: Token IDs (shape: [batch_size, sequence_length])
- attention_mask: Binary mask for padding (shape: [batch_size, sequence_length])
- token_type_ids: Segment IDs for sentence pairs (shape: [batch_size, sequence_length])
Model Outputs
- Hidden states from the last transformer layer (shape: [batch_size, sequence_length, 768])
- [CLS] token representation for sequence classification tasks
Limitations and Biases
This is a dummy model created for testing purposes and does not represent a real, trained model. It has not been trained on any data and produces random outputs.
Training Data
None - this model was generated as test data.
Evaluation Results
Not applicable - this is a test model.
Environmental Impact
Minimal environmental impact - this is a test model used only for software development and testing.
How to Get Started
from transformers import AutoTokenizer, AutoModelForMaskedLM
model_id = "your-username/test-model"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForMaskedLM.from_pretrained(model_id)
# This model is not trained, so outputs are random
inputs = tokenizer("Hello, world!", return_tensors="pt")
outputs = model(**inputs)
Model Card Contact
For issues related to this test model, please open an issue on the dmf-ng repository.
Note: This is a test artifact. For production models, ensure comprehensive model cards with real training data, evaluation metrics, and bias analysis.