Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use penscola/tweet_sentiments_analysis_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use penscola/tweet_sentiments_analysis_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="penscola/tweet_sentiments_analysis_bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("penscola/tweet_sentiments_analysis_bert") model = AutoModelForSequenceClassification.from_pretrained("penscola/tweet_sentiments_analysis_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 4
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 433318705
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bd27364f875b493d3f74c11268f01abe0a80119e5744c16e61b53ddc2215bb66
|
| 3 |
size 433318705
|
runs/Jul23_06-45-46_30fb4c0892ab/events.out.tfevents.1690094767.30fb4c0892ab.190.0
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 sha256:441b907d86ce70db3200ae911d82404500d7d590d2931648bf49a253c5964900
|
| 3 |
+
size 6779
|