Instructions to use LSimmons/FragZONFactMetaRecommendationHyperParamSearch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LSimmons/FragZONFactMetaRecommendationHyperParamSearch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LSimmons/FragZONFactMetaRecommendationHyperParamSearch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LSimmons/FragZONFactMetaRecommendationHyperParamSearch") model = AutoModelForSequenceClassification.from_pretrained("LSimmons/FragZONFactMetaRecommendationHyperParamSearch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- pytorch_model.bin +1 -1
- training_args.bin +1 -1
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1343090414
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:65e8f3ebb783de22e7c9f1a5d699e236055b81a705cc272fc7f47d97cb6a842e
|
| 3 |
size 1343090414
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 4600
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:958f3375c98df693ae0c2f25a01c1526c58c62489ba03b5ef6d7400cef843933
|
| 3 |
size 4600
|