Instructions to use thinhkosay/sentiment-bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thinhkosay/sentiment-bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="thinhkosay/sentiment-bert-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("thinhkosay/sentiment-bert-base-uncased") model = AutoModelForSequenceClassification.from_pretrained("thinhkosay/sentiment-bert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 0
Browse files- model.safetensors +1 -1
- training_args.bin +1 -1
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 437961724
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4a98e29b28a1ad0a9e698527398147c10e2c495de631290b869aa5ca84d801ea
|
| 3 |
size 437961724
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 4984
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:11f259e8c2a65631fa2704ee3bc3f152c386ade44f75dc340702fdbb3cc6f3aa
|
| 3 |
size 4984
|