Text Classification
Transformers
Safetensors
English
qwen3
text-generation
finance
earnings-calls
evasion-detection
nlp
Eval Results
text-embeddings-inference
Instructions to use FutureMa/Eva-4B-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FutureMa/Eva-4B-V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FutureMa/Eva-4B-V2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("FutureMa/Eva-4B-V2") model = AutoModelForCausalLM.from_pretrained("FutureMa/Eva-4B-V2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 958855234dbc49b85e2ea9293f928d384eff31ddff32f7fbe12b5597a12f3744
- Size of remote file:
- 7.31 kB
- SHA256:
- 06ce2aa39746d0e81f2d1ef996d3c279d6be89ddc99cd768fd9388df5406db7a
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