Text Classification
Transformers
Safetensors
English
emcoder
feature-extraction
emotion-recognition
bayesian-deep-learning
mc-dropout
uncertainty-quantification
multi-label-classification
custom_code
Eval Results (legacy)
Instructions to use yezdata/EmCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yezdata/EmCoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yezdata/EmCoder", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yezdata/EmCoder", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,028 Bytes
296800d | 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 28 29 30 31 32 33 34 35 36 37 38 | from transformers import PretrainedConfig
class EmCoderConfig(PretrainedConfig):
model_type = "emcoder"
def __init__(
self,
vocab_size=50265,
max_seq_len=512,
d_model=768,
n_head=12,
n_layers=6,
d_ffn=3072,
dropout=0.15,
num_labels=28,
base_encoder_path="",
id2label=None,
label2id=None,
**kwargs
):
# id2label konverze na int klíče (kvůli JSON standardu)
if id2label is not None:
id2label = {int(k): v for k, v in id2label.items()}
super().__init__(
id2label=id2label,
label2id=label2id,
**kwargs
)
self.vocab_size = vocab_size
self.max_seq_len = max_seq_len
self.d_model = d_model
self.n_head = n_head
self.n_layers = n_layers
self.d_ffn = d_ffn
self.dropout = dropout
self.num_labels = num_labels
self.base_encoder_path = base_encoder_path |