Instructions to use Jeevesh8/bert-base-uncased_mnli_ft_24 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert-base-uncased_mnli_ft_24 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert-base-uncased_mnli_ft_24")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert-base-uncased_mnli_ft_24") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert-base-uncased_mnli_ft_24", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Saving weights and logs of step 30000
Browse files
events.out.tfevents.1650213680.gr019.hpc.nyu.edu.239121.0.v2
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:f7047d58ed85c7e9466a00fe7f65dfe0edb27b82c7b8badc49c7d3cd5ad725cd
|
| 3 |
+
size 4397342
|
flax_model.msgpack
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 437945404
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:25c6c9ae81af683a2dfd53714bb543ee05cc1b2f945d50693cda1df0e0319afa
|
| 3 |
size 437945404
|