Text Classification
Transformers
TensorBoard
Safetensors
English
bert
cross-encoder
sequence-classification
text-embeddings-inference
Instructions to use xpmir/cross-encoder-bert-base-BCE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xpmir/cross-encoder-bert-base-BCE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xpmir/cross-encoder-bert-base-BCE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xpmir/cross-encoder-bert-base-BCE") model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-bert-base-BCE") - Notebooks
- Google Colab
- Kaggle
File size: 532 Bytes
84e584c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | accelerator: auto
checkpoint_interval: 100
loss: bce
max_grad_norm: 1.0
optimization:
batch_size: 32
eps: 1.0e-08
lr: 1.0e-05
max_epochs: 2000
num_warmup_steps: 5000
optimizer_name: adam-w
re_no_l2_regularization:
- \.bias$
- \.LayerNorm\.
scheduler: true
steps_per_epoch: 100
warmup_min_factor: 0.0
weight_decay: 0.0
precision: null
requirements: duration=20h & cpu(cores=16) & cuda(mem=50G)
sample_max: 0
sample_rate: 1.0
strategy: auto
validation: nanobeir
validation_interval: 50
validation_top_k: 100
|