Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
Transformers
English
bert
feature-extraction
agent-routing
conversation-matching
text-embeddings-inference
Instructions to use msugimura/gatekeeper_agent_responding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use msugimura/gatekeeper_agent_responding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("msugimura/gatekeeper_agent_responding") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use msugimura/gatekeeper_agent_responding with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("msugimura/gatekeeper_agent_responding") model = AutoModel.from_pretrained("msugimura/gatekeeper_agent_responding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 752 Bytes
599cfe4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | {
"best_global_step": null,
"best_metric": null,
"best_model_checkpoint": null,
"epoch": 1.0,
"eval_steps": 500,
"global_step": 54,
"is_hyper_param_search": false,
"is_local_process_zero": true,
"is_world_process_zero": true,
"log_history": [],
"logging_steps": 100,
"max_steps": 54,
"num_input_tokens_seen": 0,
"num_train_epochs": 1,
"save_steps": 500,
"stateful_callbacks": {
"TrainerControl": {
"args": {
"should_epoch_stop": false,
"should_evaluate": false,
"should_log": false,
"should_save": true,
"should_training_stop": true
},
"attributes": {}
}
},
"total_flos": 0.0,
"train_batch_size": 16,
"trial_name": null,
"trial_params": null
}
|