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twright8
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setfit-oversample-lobbying

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files
xet
Community
twright8 commited on
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Add SetFit model

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Files changed (5) hide show
  1. README.md +7 -66
  2. config.json +1 -1
  3. config_setfit.json +2 -2
  4. model.safetensors +1 -1
  5. model_head.pkl +2 -2
README.md CHANGED
@@ -9,15 +9,7 @@ tags:
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  - sentence-transformers
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  - text-classification
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  - generated_from_setfit_trainer
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- widget:
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- - text: To make introductions between Camelot's Chairman and the Cabinet Secretary.
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- We discussed the operation of the UK National Lottery and how to maximise returns
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- to National Lottery Good Causes as well as our plans to celebrate the 25th birthday
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- of The National Lottery.
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- - text: Discussion on crime
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- - text: To discuss Northern Powerhouse Rail and HS2
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- - text: To discuss food security
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- - text: Electricity market
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  inference: false
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  model-index:
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  - name: SetFit
@@ -31,10 +23,10 @@ model-index:
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  split: test
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  metrics:
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  - type: f1
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- value: 0.92
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  name: F1
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  - type: accuracy
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- value: 0.9658119658119658
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  name: Accuracy
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  ---
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@@ -68,9 +60,9 @@ The model has been trained using an efficient few-shot learning technique that i
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  ## Evaluation
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  ### Metrics
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- | Label | F1 | Accuracy |
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- |:--------|:-----|:---------|
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- | **all** | 0.92 | 0.9658 |
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  ## Uses
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@@ -90,7 +82,7 @@ from setfit import SetFitModel
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  # Download from the 🤗 Hub
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  model = SetFitModel.from_pretrained("twright8/setfit-oversample-lobbying")
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  # Run inference
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- preds = model("Electricity market")
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  ```
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  <!--
@@ -119,57 +111,6 @@ preds = model("Electricity market")
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  ## Training Details
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- ### Training Set Metrics
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- | Training set | Min | Median | Max |
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- |:-------------|:----|:--------|:----|
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- | Word count | 2 | 26.1406 | 153 |
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-
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- ### Training Hyperparameters
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- - batch_size: (16, 2)
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- - num_epochs: (4, 9)
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- - max_steps: -1
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- - sampling_strategy: oversampling
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- - body_learning_rate: (1.0797496673911536e-05, 3.457046714445997e-05)
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- - head_learning_rate: 0.0004470582121407239
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- - loss: CoSENTLoss
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- - distance_metric: cosine_distance
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- - margin: 0.25
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- - end_to_end: True
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- - use_amp: False
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- - warmup_proportion: 0.1
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- - seed: 42
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- - eval_max_steps: -1
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- - load_best_model_at_end: True
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-
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- ### Training Results
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- | Epoch | Step | Training Loss | Validation Loss |
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- |:-------:|:-------:|:-------------:|:---------------:|
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- | 0.0040 | 1 | 19.1843 | - |
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- | 0.2024 | 50 | 11.3434 | - |
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- | 0.4049 | 100 | 9.3116 | - |
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- | 0.6073 | 150 | 2.7233 | - |
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- | 0.8097 | 200 | 1.5662 | - |
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- | **1.0** | **247** | **-** | **14.3603** |
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- | 1.0121 | 250 | 0.0159 | - |
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- | 1.2146 | 300 | 0.0135 | - |
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- | 1.4170 | 350 | 0.0003 | - |
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- | 1.6194 | 400 | 0.0002 | - |
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- | 1.8219 | 450 | 0.0007 | - |
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- | 2.0 | 494 | - | 16.8205 |
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- | 2.0243 | 500 | 0.0023 | - |
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- | 2.2267 | 550 | 0.0004 | - |
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- | 2.4291 | 600 | 0.0001 | - |
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- | 2.6316 | 650 | 0.0 | - |
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- | 2.8340 | 700 | 0.0003 | - |
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- | 3.0 | 741 | - | 15.2312 |
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- | 3.0364 | 750 | 0.0 | - |
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- | 3.2389 | 800 | 3.1257 | - |
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- | 3.4413 | 850 | 0.0001 | - |
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- | 3.6437 | 900 | 0.0002 | - |
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- | 3.8462 | 950 | 0.0139 | - |
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- | 4.0 | 988 | - | 14.4995 |
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-
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- * The bold row denotes the saved checkpoint.
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  ### Framework Versions
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  - Python: 3.10.12
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  - SetFit: 1.0.3
 
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  - sentence-transformers
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  - text-classification
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  - generated_from_setfit_trainer
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+ widget: []
 
 
 
 
 
 
 
 
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  inference: false
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  model-index:
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  - name: SetFit
 
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  split: test
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  metrics:
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  - type: f1
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+ value: 0.9411764705882353
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  name: F1
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  - type: accuracy
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+ value: 0.9743589743589743
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  name: Accuracy
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  ---
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  ## Evaluation
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  ### Metrics
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+ | Label | F1 | Accuracy |
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+ |:--------|:-------|:---------|
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+ | **all** | 0.9412 | 0.9744 |
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  ## Uses
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  # Download from the 🤗 Hub
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  model = SetFitModel.from_pretrained("twright8/setfit-oversample-lobbying")
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  # Run inference
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+ preds = model("I loved the spiderman movie!")
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  ```
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  <!--
 
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  ## Training Details
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  ### Framework Versions
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  - Python: 3.10.12
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  - SetFit: 1.0.3
config.json CHANGED
@@ -1,5 +1,5 @@
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  "BertModel"
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config_setfit.json CHANGED
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