Instructions to use Ojeda01/bert_base_cased_MultiClass_Sentiment_A with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ojeda01/bert_base_cased_MultiClass_Sentiment_A with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ojeda01/bert_base_cased_MultiClass_Sentiment_A")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ojeda01/bert_base_cased_MultiClass_Sentiment_A") model = AutoModelForSequenceClassification.from_pretrained("Ojeda01/bert_base_cased_MultiClass_Sentiment_A") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 2000
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 433412341
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bc4fdd4a7fca8a186a18aa8a3e83c4453d58f802a6ef0e81788ee18c6a86d457
|
| 3 |
size 433412341
|
runs/Feb22_22-21-18_ac1f95d9de4a/events.out.tfevents.1677104486.ac1f95d9de4a.329.2
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1138a291190a4310bcef21cbe5e706f5f7e047d419217a4a517f980c42568a28
|
| 3 |
+
size 7959
|