Instructions to use research-dump/distilbert_base_cased_temp_classifier_bootstrapped_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use research-dump/distilbert_base_cased_temp_classifier_bootstrapped_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="research-dump/distilbert_base_cased_temp_classifier_bootstrapped_v3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("research-dump/distilbert_base_cased_temp_classifier_bootstrapped_v3") model = AutoModelForSequenceClassification.from_pretrained("research-dump/distilbert_base_cased_temp_classifier_bootstrapped_v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:545a06925bec5b6ee9f1577b869b4d55a403b21e04e2f8d14e1cdc5792389e0d
|
| 3 |
+
size 263160052
|