Instructions to use abigailp/m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abigailp/m3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="abigailp/m3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("abigailp/m3") model = AutoModelForSequenceClassification.from_pretrained("abigailp/m3") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 3
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 267855533
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d8dcb2f028b15d78bc3a00c9581396bc50274339453484206a18b4b68b8dcffd
|
| 3 |
size 267855533
|
runs/Jan31_12-54-12_f2ffbcdf767c/events.out.tfevents.1675169659.f2ffbcdf767c.234.9
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:e030c6ba873ede87bf364d422953800f46794aece309d120f95dc9b07e6438eb
|
| 3 |
+
size 4743
|