Text Classification
Transformers
Safetensors
GGUF
English
bert
email
triage
classification
specific-ai
text-embeddings-inference
feature-extraction
Instructions to use specific-AI/email-agent-triage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use specific-AI/email-agent-triage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="specific-AI/email-agent-triage")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("specific-AI/email-agent-triage") model = AutoModelForSequenceClassification.from_pretrained("specific-AI/email-agent-triage", device_map="auto") - llama-cpp-python
How to use specific-AI/email-agent-triage with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="specific-AI/email-agent-triage", filename="bert-base-only.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use specific-AI/email-agent-triage with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf specific-AI/email-agent-triage # Run inference directly in the terminal: llama cli -hf specific-AI/email-agent-triage
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf specific-AI/email-agent-triage # Run inference directly in the terminal: llama cli -hf specific-AI/email-agent-triage
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf specific-AI/email-agent-triage # Run inference directly in the terminal: ./llama-cli -hf specific-AI/email-agent-triage
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf specific-AI/email-agent-triage # Run inference directly in the terminal: ./build/bin/llama-cli -hf specific-AI/email-agent-triage
Use Docker
docker model run hf.co/specific-AI/email-agent-triage
- LM Studio
- Jan
- Ollama
How to use specific-AI/email-agent-triage with Ollama:
ollama run hf.co/specific-AI/email-agent-triage
- Unsloth Studio
How to use specific-AI/email-agent-triage with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for specific-AI/email-agent-triage to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for specific-AI/email-agent-triage to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for specific-AI/email-agent-triage to start chatting
- Atomic Chat new
- Docker Model Runner
How to use specific-AI/email-agent-triage with Docker Model Runner:
docker model run hf.co/specific-AI/email-agent-triage
- Lemonade
How to use specific-AI/email-agent-triage with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull specific-AI/email-agent-triage
Run and chat with the model
lemonade run user.email-agent-triage-{{QUANT_TAG}}List all available models
lemonade list
File size: 925 Bytes
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"BertForSequenceClassification"
],
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"dtype": "float32",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "FYI",
"1": "NEEDS_RESPONSE",
"2": "PERSONAL",
"3": "PROMOTIONAL",
"4": "URGENT"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
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"PERSONAL": 2,
"PROMOTIONAL": 3,
"URGENT": 4
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"transformers_version": "4.57.3",
"type_vocab_size": 2,
"use_cache": true,
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}
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