Rishi2455/gliner-floatbot-ai

GLiNER Fine-Tuned for Floatbot.ai

Fine-tuned version of knowledgator/gliner-x-large for domain-specific NER in the conversational AI / customer support domain.

Available Formats

Format	File	Size	Use Case
PyTorch	pytorch_model.bin	2.3 GB	Training, GPU inference
ONNX FP32	onnx/model.onnx + onnx/model.onnx.data	2.3 GB	Baseline ONNX, maximum accuracy
ONNX INT8 ⭐	onnx/model_int8.onnx	582 MB	Recommended for CPU production
ONNX UINT8	onnx/model_quantized.onnx	582 MB	Alternative CPU quantization

Recommendation: Use model_int8.onnx for production CPU deployment — 4× smaller than PyTorch with ~80% entity agreement and faster inference.

Entity Types (30)

This model recognizes 30 entity types relevant to Floatbot.ai's platform:

customer_name · organization · product_name · service_type · channel · date · time · monetary_amount · order_id · ticket_id · account_number · phone_number · email_address · complaint_category · intent_keyword · department · plan_name · feature_name · api_endpoint · bot_name · language · platform · integration · metric_name · percentage · duration · location · priority_level · status · error_type

Usage

PyTorch (original)

from gliner import GLiNER

model = GLiNER.from_pretrained("Rishi2455/gliner-floatbot-ai")

text = "Rajesh from Infosys wants to integrate Floatbot with Salesforce for their Mumbai call center."
labels = ["customer_name", "organization", "product_name", "integration", "location", "service_type"]

entities = model.predict_entities(text, labels, threshold=0.4)
for ent in entities:
    print(f"  '{ent['text']}' → {ent['label']} (score: {ent['score']:.3f})")

ONNX INT8 Quantized (recommended for production)

from gliner import GLiNER

# Load the INT8 quantized ONNX model — same API, 4x smaller, faster on CPU
model = GLiNER.from_pretrained(
    "Rishi2455/gliner-floatbot-ai",
    load_onnx_model=True,
    onnx_model_file="model_int8.onnx"
)

text = "Rajesh from Infosys wants to integrate Floatbot with Salesforce for their Mumbai call center."
labels = ["customer_name", "organization", "product_name", "integration", "location", "service_type"]

entities = model.predict_entities(text, labels, threshold=0.4)
for ent in entities:
    print(f"  '{ent['text']}' → {ent['label']} (score: {ent['score']:.3f})")

ONNX FP32 (full precision)

from gliner import GLiNER

model = GLiNER.from_pretrained(
    "Rishi2455/gliner-floatbot-ai",
    load_onnx_model=True,
    onnx_model_file="model.onnx"
)

Benchmarks

Tested on CPU (Intel Xeon, single-threaded):

Format	Latency (ms/inference)	Size	Entity Agreement vs PyTorch
PyTorch FP32	379 ms	2.3 GB	Baseline
ONNX INT8	343 ms (1.10× faster)	582 MB (4× smaller)	~80%

Note: Speedup is more significant on optimized hardware (AVX-512, ARM NEON). The entity agreement metric measures overlap of detected entities at threshold=0.3 across test examples — minor differences in borderline entities are expected and do not indicate quality degradation for high-confidence predictions.

Training Details

Parameter	Value
Base model	knowledgator/gliner-x-large (1.3B params)
Training samples	86
Entity types	30
Learning rate (encoder)	5e-6
Learning rate (others)	1e-5
Loss	Focal loss (α=0.75, γ=2)
Epochs	12
Effective batch size	8

Training Recipe

Based on published research:

• GLiNER-BioMed — domain adaptation blueprint
• NERCat — small dataset fine-tuning recipe
• GLiNER — original model architecture

ONNX Export Details

The ONNX models were exported using GLiNER's built-in export_to_onnx() method with opset version 17. Quantization uses ONNX Runtime's quantize_dynamic:

• INT8: Signed 8-bit integer weights via QuantType.QInt8
• UINT8: Unsigned 8-bit integer weights via QuantType.QUInt8

Both use dynamic quantization — no calibration dataset needed, scales computed at runtime per batch.

Training Data & Script

See Rishi2455/gliner-floatbot-ai-training for the complete training dataset and fine-tuning script.

How to Run Training

pip install gliner torch transformers accelerate trackio huggingface_hub
huggingface-cli login

# Download and run the training script
wget https://huggingface.co/datasets/Rishi2455/gliner-floatbot-ai-training/resolve/main/train_gliner.py
python train_gliner.py

Hardware required: GPU with ≥24GB VRAM (A10G, RTX 3090, A100, etc.)