Push model using huggingface_hub.

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: patrickfleith/my-awesome-astro-text-classifier
+base_model: BAAI/bge-small-en-v1.5
 library_name: setfit
 metrics:
 - accuracy
@@ -9,13 +9,38 @@ tags:
 - sentence-transformers
 - text-classification
 - generated_from_setfit_trainer
-widget: []
+widget:
+- text: How does the choice of oxidizer, such as liquid oxygen or nitrogen tetroxide,
+    affect the performance and handling requirements of a rocket engine?
+- text: Rocket engines designed for vacuum operation often incorporate radiative cooling
+    methods, utilizing large surface areas to dissipate heat in the absence of convective
+    cooling mechanisms.
+- text: Thermo-optical properties of surface materials, such as absorptivity and emissivity,
+    are critical parameters in the design of the thermal control subsystem.
+- text: The thrust produced by a rocket engine is a function of the mass flow rate
+    of the propellant and the velocity of the exhaust gases as they exit the nozzle.
+- text: Thermal analysis of a satellite involves finite element modeling to predict
+    temperature gradients and ensure proper thermal design and component placement.
 inference: true
+model-index:
+- name: SetFit with BAAI/bge-small-en-v1.5
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 1.0
+      name: Accuracy
 ---
 
-# SetFit with patrickfleith/my-awesome-astro-text-classifier
+# SetFit with BAAI/bge-small-en-v1.5
 
-This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [patrickfleith/my-awesome-astro-text-classifier](https://huggingface.co/patrickfleith/my-awesome-astro-text-classifier) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
 
 The model has been trained using an efficient few-shot learning technique that involves:
 
@@ -26,7 +51,7 @@ The model has been trained using an efficient few-shot learning technique that i
 
 ### Model Description
 - **Model Type:** SetFit
-- **Sentence Transformer body:** [patrickfleith/my-awesome-astro-text-classifier](https://huggingface.co/patrickfleith/my-awesome-astro-text-classifier)
+- **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
 - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
 - **Maximum Sequence Length:** 512 tokens
 - **Number of Classes:** 3 classes
@@ -40,6 +65,20 @@ The model has been trained using an efficient few-shot learning technique that i
 - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
 - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
 
+### Model Labels
+| Label           | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+|:----------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Propulsion      | <ul><li>"Rocket engines operate on the principle of Newton's Third Law of Motion, where the expulsion of high-speed exhaust gases produces a reaction force that propels the rocket forward."</li><li>'The combustion efficiency of a rocket engine depends on factors like propellant mixture ratio, injector design, and combustion chamber pressure.'</li><li>'Deep throttling capability, which allows a rocket engine to vary its thrust over a wide range, is essential for applications requiring precise landing maneuvers, such as lunar landers.'</li></ul>                          |
+| Power Subsystem | <ul><li>'Redundant power paths and autonomous fault detection mechanisms are implemented to ensure continuous electrical supply even in the event of subsystem failures or external anomalies.'</li><li>'Electromagnetic interference (EMI) shielding and grounding techniques are essential in satellite design to prevent power system noise from affecting sensitive communication and navigation subsystems.'</li><li>'Autonomous diagnostic and recovery protocols are embedded within the power management system to isolate and rectify faults, ensuring mission continuity.'</li></ul> |
+| Thermal Control | <ul><li>'The thermal control subsystem must accommodate both internal heat generated by electronic components and external thermal loads from the space environment.'</li><li>'Describe the impact of albedo and infrared emissions from Earth on satellite thermal design.'</li><li>'Passive thermal control elements, such as multi-layer insulation (MLI), surface coatings, and radiators, are used to minimize thermal fluctuations and radiation absorption.'</li></ul>                                                                                                                  |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 1.0      |
+
 ## Uses
 
 ### Direct Use for Inference
@@ -58,7 +97,7 @@ from setfit import SetFitModel
 # Download from the 🤗 Hub
 model = SetFitModel.from_pretrained("patrickfleith/my-awesome-astro-text-classifier")
 # Run inference
-preds = model("I loved the spiderman movie!")
+preds = model("How does the choice of oxidizer, such as liquid oxygen or nitrogen tetroxide, affect the performance and handling requirements of a rocket engine?")
 ```
 
 <!--
@@ -87,6 +126,45 @@ preds = model("I loved the spiderman movie!")
 
 ## Training Details
 
+### Training Set Metrics
+| Training set | Min | Median  | Max |
+|:-------------|:----|:--------|:----|
+| Word count   | 11  | 22.2368 | 30  |
+
+| Label           | Training Sample Count |
+|:----------------|:----------------------|
+| Propulsion      | 15                    |
+| Thermal Control | 14                    |
+| Power Subsystem | 9                     |
+
+### Training Hyperparameters
+- batch_size: (32, 32)
+- num_epochs: (10, 10)
+- max_steps: -1
+- sampling_strategy: oversampling
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0333 | 1    | 0.2377        | -               |
+| 1.6667 | 50   | 0.0551        | -               |
+| 3.3333 | 100  | 0.0046        | -               |
+| 5.0    | 150  | 0.0031        | -               |
+| 6.6667 | 200  | 0.0024        | -               |
+| 8.3333 | 250  | 0.0022        | -               |
+| 10.0   | 300  | 0.002         | -               |
+
 ### Framework Versions
 - Python: 3.10.12
 - SetFit: 1.0.3

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "patrickfleith/my-awesome-astro-text-classifier",
+  "_name_or_path": "BAAI/bge-small-en-v1.5",
   "architectures": [
     "BertModel"
   ],

config_setfit.json

@@ -1,8 +1,8 @@
 {
-  "normalize_embeddings": false,
   "labels": [
     "Propulsion",
     "Thermal Control",
     "Power Subsystem"
-  ]
+  ],
+  "normalize_embeddings": false
 }

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:20dfc9830ecb5f48de13002e979b34f400b7464bf88f3f45681deb68998b7551
 size 133462128

model_head.pkl

@@ -1,3 +1,3 @@
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+oid sha256:f8213047d5e6788e2b87b6291b19cfe913e64a14a25fdc5fad543009527f02fa
 size 10255

tokenizer_config.json

@@ -46,19 +46,12 @@
   "do_basic_tokenize": true,
   "do_lower_case": true,
   "mask_token": "[MASK]",
-  "max_length": 512,
   "model_max_length": 512,
   "never_split": null,
-  "pad_to_multiple_of": null,
   "pad_token": "[PAD]",
-  "pad_token_type_id": 0,
-  "padding_side": "right",
   "sep_token": "[SEP]",
-  "stride": 0,
   "strip_accents": null,
   "tokenize_chinese_chars": true,
   "tokenizer_class": "BertTokenizer",
-  "truncation_side": "right",
-  "truncation_strategy": "longest_first",
   "unk_token": "[UNK]"
 }