Instructions to use zfan3/esm2_35M_CST_split20_checkpoint-1900 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zfan3/esm2_35M_CST_split20_checkpoint-1900 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zfan3/esm2_35M_CST_split20_checkpoint-1900")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zfan3/esm2_35M_CST_split20_checkpoint-1900") model = AutoModelForSequenceClassification.from_pretrained("zfan3/esm2_35M_CST_split20_checkpoint-1900", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload EsmForSequenceClassification
Browse files- config.json +1 -1
- model.safetensors +1 -1
config.json
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "./
|
| 3 |
"architectures": [
|
| 4 |
"EsmForSequenceClassification"
|
| 5 |
],
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "./esm2_t12_35M_CST_split20_high_learning/checkpoint-4750",
|
| 3 |
"architectures": [
|
| 4 |
"EsmForSequenceClassification"
|
| 5 |
],
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 135998360
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bc8da50e2f033cf73fb71ed2cdcb4bb9740a39ec81cc775fa2c5dc7801e4e8db
|
| 3 |
size 135998360
|