Feature Extraction
Transformers
TensorBoard
Safetensors
English
captionbert_v2
sentence-similarity
consensus-distillation
geometric-deep-learning
amoe
custom_code
Instructions to use AbstractPhil/captionbert-8192-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbstractPhil/captionbert-8192-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AbstractPhil/captionbert-8192-v2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AbstractPhil/captionbert-8192-v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,398 Bytes
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"bert": {
"in_sample": {
"cos": 1.0,
"r1": 0.9965000152587891,
"n": 24000,
"chance": 0.0005
},
"out_of_sample": {
"cos": 1.0,
"r1": 0.9909999966621399,
"n": 8000,
"chance": 0.0005
}
},
"modern": {
"in_sample": {
"cos": 0.38715124130249023,
"r1": 0.9649999737739563,
"n": 24000,
"chance": 0.0005
},
"out_of_sample": {
"cos": 0.3266415596008301,
"r1": 0.9164999723434448,
"n": 8000,
"chance": 0.0005
}
},
"roberta": {
"in_sample": {
"cos": 0.4255034923553467,
"r1": 0.9810000061988831,
"n": 24000,
"chance": 0.0005
},
"out_of_sample": {
"cos": 0.37222719192504883,
"r1": 0.9480000138282776,
"n": 8000,
"chance": 0.0005
}
},
"albert": {
"in_sample": {
"cos": 0.3922822177410126,
"r1": 0.9785000085830688,
"n": 24000,
"chance": 0.0005
},
"out_of_sample": {
"cos": 0.3313330411911011,
"r1": 0.9384999871253967,
"n": 8000,
"chance": 0.0005
}
},
"distil": {
"in_sample": {
"cos": 0.6433802247047424,
"r1": 0.9955000281333923,
"n": 24000,
"chance": 0.0005
},
"out_of_sample": {
"cos": 0.6245564818382263,
"r1": 0.9810000061988831,
"n": 8000,
"chance": 0.0005
}
}
} |