Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Cielciel/aift-model-review-multiple-label-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cielciel/aift-model-review-multiple-label-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cielciel/aift-model-review-multiple-label-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cielciel/aift-model-review-multiple-label-classification") model = AutoModelForSequenceClassification.from_pretrained("Cielciel/aift-model-review-multiple-label-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,051 Bytes
bbc5ecf | 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 | # Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the \"License\");
# you may not use this file except in compliance with the License.\n",
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an \"AS IS\" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from transformers import AutoModelForSequenceClassification
from trainer import metadata
def create(num_labels):
"""create the model by loading a pretrained model or define your
own
Args:
num_labels: number of target labels
"""
# Create the model, loss function, and optimizer
model = AutoModelForSequenceClassification.from_pretrained(
metadata.PRETRAINED_MODEL_NAME,
num_labels=num_labels
)
return model
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