Instructions to use tweettemposhift/nerd-nerd_random3_seed0-bertweet-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tweettemposhift/nerd-nerd_random3_seed0-bertweet-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tweettemposhift/nerd-nerd_random3_seed0-bertweet-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tweettemposhift/nerd-nerd_random3_seed0-bertweet-base") model = AutoModelForSequenceClassification.from_pretrained("tweettemposhift/nerd-nerd_random3_seed0-bertweet-base") - Notebooks
- Google Colab
- Kaggle
commit files to HF hub
Browse files- summary.json +1 -0
- training_args.bin +3 -0
summary.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"test/eval_loss": 0.38369354605674744, "test/eval_f1": 0.8603231597845601, "test/eval_accuracy": 0.8535942792623259, "test/eval_runtime": 28.3226, "test/eval_samples_per_second": 187.624, "test/eval_steps_per_second": 11.757}
|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a127088065fe43be37af4b33825e5b86051f63459b8d47d1338053bff9845d4c
|
| 3 |
+
size 4536
|