Instructions to use Exscientia/IgBert_unpaired with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Exscientia/IgBert_unpaired with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Exscientia/IgBert_unpaired")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Exscientia/IgBert_unpaired") model = AutoModelForMaskedLM.from_pretrained("Exscientia/IgBert_unpaired", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload BertForMaskedLM
Browse files- config.json +1 -1
- generation_config.json +5 -0
- model.safetensors +2 -2
config.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
"_name_or_path": "/exs/shared/collaboration/projects/ablm/private/dgx_models/ig_bert_unpaired/",
|
| 3 |
"architectures": [
|
| 4 |
-
"
|
| 5 |
],
|
| 6 |
"attention_probs_dropout_prob": 0.0,
|
| 7 |
"classifier_dropout": null,
|
|
|
|
| 1 |
{
|
| 2 |
"_name_or_path": "/exs/shared/collaboration/projects/ablm/private/dgx_models/ig_bert_unpaired/",
|
| 3 |
"architectures": [
|
| 4 |
+
"BertForMaskedLM"
|
| 5 |
],
|
| 6 |
"attention_probs_dropout_prob": 0.0,
|
| 7 |
"classifier_dropout": null,
|
generation_config.json
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"pad_token_id": 0,
|
| 4 |
+
"transformers_version": "4.36.1"
|
| 5 |
+
}
|
model.safetensors
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 sha256:0073dac1e5cf24ceac37099715f8ecc9c27c87d0065a78bfec1ab75aae534e7b
|
| 3 |
+
size 1679791776
|