Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use ViktorDo/DistilBERT-POWO_Growth_Form_Finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViktorDo/DistilBERT-POWO_Growth_Form_Finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ViktorDo/DistilBERT-POWO_Growth_Form_Finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ViktorDo/DistilBERT-POWO_Growth_Form_Finetuned") model = AutoModelForSequenceClassification.from_pretrained("ViktorDo/DistilBERT-POWO_Growth_Form_Finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 500
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 267858605
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d160d82fb19bb4a5c2f558bd29fef173a21db13a995449e8ce8b55e8cadbae22
|
| 3 |
size 267858605
|
runs/May19_19-27-42_41b058e6ff99/1684524466.84684/events.out.tfevents.1684524466.41b058e6ff99.3817.1
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c6794659cae93409694f66f4d0dc3365245720f98069978f3026064d3f08e402
|
| 3 |
+
size 5934
|
runs/May19_19-27-42_41b058e6ff99/events.out.tfevents.1684524466.41b058e6ff99.3817.0
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5836e6e23a548a1afded2d7197d660c93db048caddc8324ac0a5032f0e6636aa
|
| 3 |
+
size 4247
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 3643
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:156f92820df06a0206a20bb795361c0d258c2bb72f5cb121e3adf43e6949f6ba
|
| 3 |
size 3643
|