Instructions to use Hikam22/Review with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hikam22/Review with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Hikam22/Review")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hikam22/Review") model = AutoModelForMaskedLM.from_pretrained("Hikam22/Review") - Notebooks
- Google Colab
- Kaggle
Upload 3 files
Browse files- config.json +19 -0
- pytorch_model.bin +3 -0
- training_args.bin +3 -0
config.json
ADDED
|
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"BertForMaskedLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_probs_dropout_prob": 0.1,
|
| 6 |
+
"hidden_act": "gelu",
|
| 7 |
+
"hidden_dropout_prob": 0.1,
|
| 8 |
+
"hidden_size": 768,
|
| 9 |
+
"initializer_range": 0.02,
|
| 10 |
+
"intermediate_size": 3072,
|
| 11 |
+
"layer_norm_eps": 1e-12,
|
| 12 |
+
"max_position_embeddings": 512,
|
| 13 |
+
"model_type": "bert",
|
| 14 |
+
"num_attention_heads": 12,
|
| 15 |
+
"num_hidden_layers": 6,
|
| 16 |
+
"pad_token_id": 0,
|
| 17 |
+
"type_vocab_size": 2,
|
| 18 |
+
"vocab_size": 50000
|
| 19 |
+
}
|
pytorch_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bc05cfa7c15d0bebe2ceef5fb98982d63ed32f853e1139b63fb78ae09ae49895
|
| 3 |
+
size 330252204
|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:389349734a704424ef4fd01e2674ed03abd53c8a0dedc709ca559d46ccf4c1dc
|
| 3 |
+
size 1023
|