Text Classification
Transformers
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use mansurealism/working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mansurealism/working with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mansurealism/working")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mansurealism/working") model = AutoModelForSequenceClassification.from_pretrained("mansurealism/working", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mansurealism/working: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/mansurealism/working/resolve/main/training_args.bin
- Command line
-
hf download hf://mansurealism/working/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mansurealism/working/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 9c4eaa44ef57920b60aa4338b7ad6f5971a0be52029c6ec3a38c355ae425b903
- Size of remote file:
- 5.43 kB
- SHA256:
- 07dd56bff0945bed6ba90e670599b1fe2947ec1e417b6dd26d5f1cab5d5bfd10
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.