Instructions to use DunnBC22/fnet-base-Financial_Sentiment_Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/fnet-base-Financial_Sentiment_Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/fnet-base-Financial_Sentiment_Analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/fnet-base-Financial_Sentiment_Analysis") model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/fnet-base-Financial_Sentiment_Analysis") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 2
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 331492221
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:02b122b974b0cf267890e30ed669dc16efc82388b369285de3e3fadc8f76327d
|
| 3 |
size 331492221
|
runs/May05_21-47-27_Brians-Mac-mini/events.out.tfevents.1683341253.Brians-Mac-mini.15242.0
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 sha256:6aff0c4ac739816d3b02159a49dd51021419f92baef8898e3a44a3ab2760e117
|
| 3 |
+
size 6807
|