Text Classification
Transformers
Safetensors
distilbert
email
spam-detection
text-embeddings-inference
Instructions to use akumar0205/phishing_spam_jev_like_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akumar0205/phishing_spam_jev_like_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="akumar0205/phishing_spam_jev_like_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("akumar0205/phishing_spam_jev_like_classifier") model = AutoModelForSequenceClassification.from_pretrained("akumar0205/phishing_spam_jev_like_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download latency.json from akumar0205/phishing_spam_jev_like_classifier: direct link, hf CLI and curl.
- Browser
- Download file 1.16 kB
-
https://huggingface.co/akumar0205/phishing_spam_jev_like_classifier/resolve/main/latency.json
- Command line
-
hf download hf://akumar0205/phishing_spam_jev_like_classifier/latency.json
-
curl -L -o latency.json https://huggingface.co/akumar0205/phishing_spam_jev_like_classifier/resolve/main/latency.json
1.16 kB
| { | |
| "tokenize(receiver)": { | |
| "mean_ms": 0.285680200004208, | |
| "p50_ms": 0.28286499997420833, | |
| "p95_ms": 0.29625649997342407, | |
| "n": 50.0 | |
| }, | |
| "infer x1": { | |
| "mean_ms": 20.3907745399988, | |
| "p50_ms": 20.329225000011775, | |
| "p95_ms": 20.864236499971867, | |
| "n": 50.0 | |
| }, | |
| "decide() x1": { | |
| "mean_ms": 20.359278120006365, | |
| "p50_ms": 20.330745499961722, | |
| "p95_ms": 20.485164500007613, | |
| "n": 50.0 | |
| }, | |
| "infer x16": { | |
| "mean_ms": 459.22972584000263, | |
| "p50_ms": 461.296622500015, | |
| "p95_ms": 472.11640399995076, | |
| "n": 50.0 | |
| }, | |
| "throughput_emails_per_sec": { | |
| "bs_1": 50.55320677490372, | |
| "bs_8": 33.17495789514291, | |
| "bs_32": 34.04478551894138 | |
| }, | |
| "device": "cpu", | |
| "benchmark_conditions": { | |
| "device": "cpu", | |
| "gpu_name": "NVIDIA RTX PRO 6000 Blackwell Server Edition", | |
| "torch": "2.11.0+cu130", | |
| "cuda_available": true, | |
| "cuda_version": "13.0", | |
| "python": "3.13.15 (main, Aug 6 2026, 11:06:22) [GCC 13.3.0]", | |
| "max_length": 512, | |
| "reps_per_stage": 50, | |
| "warmup": 5, | |
| "pool_size": 64, | |
| "pool_source": "testbeds", | |
| "date_utc": "2026-10-16T02:48:52.151664+00:00" | |
| } | |
| } |