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tensorflow_tensorflow_model
nilcars/tensorflow_tensorflow_model
tensorflow_tensorflow_model is a text classification model from nilcars. Use it when you need a label for a piece of text. It is set up for setfit.
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for cl…
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At a glance
- Task
- Text Classification
- Library
- setfit
- Model type
- mpnet
- Access
- Public
- Created
- Jun 24, 2024
- Updated
- Jun 24, 2024
- SHA
- 38611da0
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Base models
Model card
From the Hugging Face model README
SetFit with sentence-transformers/all-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: sentence-transformers/all-mpnet-base-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 384 tokens
- Number of Classes: 3 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
| Label | Examples |
|---|---|
| question | <ul><li>"Parse output of mobile_ssd_v2_float_coco.tflite ### Issue type\n\nSupport\n\n### Have you reproduced the bug with TensorFlow Nightly?\n\nNo\n\n### Source\n\nsource\n\n### TensorFlow version\n\nv2.11.1\n\n### Custom code\n\nYes\n\n### OS platform and distribution\n\nLinux Ubuntu 20.04\n\n### Mobile device\n\nAndroid\n\n### Python version\n\n_No response_\n\n### Bazel version\n\n6.2.0\n\n### GCC/compiler version\n\n12\n\n### CUDA/cuDNN version\n\n_No response_\n\n### GPU model and memory\n\n_No response_\n\n### Current behavior?\n\nI'm trying to use the model mobile_ssd_v2_float_coco.tflite on a C++ application, I'm able to execute the inference and get the results.\r\n\r\nBased on the Netron app I see that its output is:\r\nshell\nNo example code is available to parse the output of mobile_ssd_v2_float_coco.tflite.\n\n\n\n### Relevant log output\n\n_No response_"</li><li>'Tensorflow Lite library is crashing in WASM library at 3rd inference <details><summary>Click to expand!</summary> \r\n \r\n ### Issue Type\r\n\r\nSupport\r\n\r\n### Have you reproduced the bug with TF nightly?\r\n\r\nYes\r\n\r\n### Source\r\n\r\nsource\r\n\r\n### Tensorflow Version\r\n\r\n2.7.0\r\n\r\n### Custom Code\r\n\r\nYes\r\n\r\n### OS Platform and Distribution\r\n\r\nEmscripten, Ubuntu 18.04\r\n\r\n### Mobile device\r\n\r\n_No response_\r\n\r\n### Python version\r\n\r\n_No response_\r\n\r\n### Bazel version\r\n\r\n_No response_\r\n\r\n### GCC/Compiler version\r\n\r\n_No response_\r\n\r\n### CUDA/cuDNN version\r\n\r\n_No response_\r\n\r\n### GPU model and memory\r\n\r\n_No response_\r\n\r\n### Current Behaviour?\r\n\r\nshell\r\nHello! I have C++ code that I want to deploy as WASM library and this code contains TFLite library. I have compiled TFLite library with XNNPack support using Emscripten toolchain quite easy, so no issue there. I have a leight-weight convolution+dense model that runs perfectly on Desktop, but I am starting having problems in the browser.\r\n\r\nIn 99% of cases I have an error on the third inference:\r\n\r\nUncaught RuntimeError: memory access out of bounds\r\n\r\nThrough some trivial debugging I have found out that the issue comes from _interpreter->Invoke() method. Does not matter if I put any input or not, I just need to call Invoke() three times and I have a crash.\r\n\r\nFirst thing first: I decided to add more memory to my WASM library by adding this line to CMake:\r\n\r\nSET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -s TOTAL_STACK=134217728 -s TOTAL_MEMORY=268435456")\r\nSET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -s TOTAL_STACK=134217728 -s TOTAL_MEMORY=268435456")\r\n\r\n128 MB and 256 MB in total for 1 MB model - I think this is more than enough. And on top of that, I am allowing Memory Growth. But unfortunately, I have exactly the same issue.\r\n\r\nI am beating on this problem for 2 weeks straight and at this stage I have no clue how to fix it. Also I have tried to set custom allocation using TfLiteCustomAllocation but in this case I have a crash on the very first inference. I guess I was not using it right, but unfortunately I couldn\'t find even one tutorial describing how to apply custom allocation in TFLite.\r\n\r\nI said that I have a crash in 99% of cases. There was one time when WASM library worked and inference worked as well. It happens just randomly once, and I couldn\'t reproduce it anymore.\r\n\r\n\r\n\r\n### Standalone code to reproduce the issue\r\n\r\n```shell\r\nHere is the code that does TFLite inference\r\n\r\n\r\n#include <cstdlib>\r\n#include "tflite_model.h"\r\n#include <iostream>\r\n\r\n#include "tensorflow/lite/interpreter.h"\r\n#include "tensorflow/lite/util.h"\r\n\r\nnamespace tracker {\r\n\r\n#ifdef EMSCRIPTEN\r\n\tvoid TFLiteModel::init(std::stringstream& stream) {\r\n\r\n\t\tstd::string img_str = stream.str();\r\n\t\tstd::vector<char> img_model_data(img_str.size());\r\n\t\tstd::copy(img_str.begin(), img_str.end(), img_model_data.begin());\r\n\r\n\t\t_model = tflite::FlatBufferModel::BuildFromBuffer(img_str.data(), img_str.size());\r\n#else\r\n\tvoid TFLiteModel::init(const std::string& path) {\r\n\t\t_model = tflite::FlatBufferModel::BuildFromFile(path.c_str());\r\n\r\n#endif\r\n\r\n\t\ttflite::ops::builtin::BuiltinOpResolver resolver;\r\n\t\ttflite::InterpreterBuilder(*_model, resolver)(&_interpreter);\r\n\r\n\t\t_interpreter->AllocateTensors();\r\n\r\n\t\t/for (int i = 0; i < _interpreter->tensors_size(); i++) {\r\n\t\t\tTfLiteTensor tensor = _interpreter->tensor(i);\r\n\r\n\t\t\tif (tensor->allocation_type == kTfLiteArenaRw |
| feature | <ul><li>'tf.keras.optimizers.experimental.AdamW only support constant weight_decay <details><summary>Click to expand!</summary> \n \n ### Issue Type\n\nFeature Request\n\n### Source\n\nsource\n\n### Tensorflow Version\n\n2.8\n\n### Custom Code\n\nNo\n\n### OS Platform and Distribution\n\n_No response_\n\n### Mobile device\n\n_No response_\n\n### Python version\n\n_No response_\n\n### Bazel version\n\n_No response_\n\n### GCC/Compiler version\n\n_No response_\n\n### CUDA/cuDNN version\n\n_No response_\n\n### GPU model and memory\n\n_No response_\n\n### Current Behaviour?\n\nshell\ntf.keras.optimizers.experimental.AdamW only supports constant weight decay. But usually we want the weight_decay value to decay with learning rate schedule.\n\n\n\n### Standalone code to reproduce the issue\n\nshell\nThe legacy tfa.optimizers.AdamW supports callable weight_decay, which is much better.\n\n\n\n### Relevant log output\n\n_No response_</details>'</li><li>'RFE tensorflow-aarch64==2.6.0 build ? System information\r\n TensorFlow version (you are using): 2.6.0\r\n- Are you willing to contribute it (Yes/No): Yes\r\n\r\nDescribe the feature and the current behavior/state.\r\n\r\nBrainchip Akida AKD1000 SNN neuromorphic MetaTF SDK support 2.6.0 on x86_64. They claim support for aarch64, but when creating a virtualenv it fails on aarch64 due to lacking tensorflow-aarc64==2.6.0 build.\r\n\r\nWill this change the current api? How?\r\n\r\nNA\r\n\r\nWho will benefit with this feature?\r\n\r\nCustomer of Brainchip Akida who run on Arm64 platforms.\r\n\r\nAny Other info.\r\n\r\nhttps://doc.brainchipinc.com/installation.html\r\n\r\n\r\n'</li><li>"How to calculate 45 degree standing position of body from camera in swift (Pose estimation) <details><summary>Click to expand!</summary> \n \n ### Issue Type\n\nFeature Request\n\n### Source\n\nsource\n\n### Tensorflow Version\n\npod 'TensorFlowLiteSwift', '~> 0.0.1-nightly', :subspecs => ['CoreML', 'Metal']\n\n### Custom Code\n\nYes\n\n### OS Platform and Distribution\n\n_No response_\n\n### Mobile device\n\n_No response_\n\n### Python version\n\n_No response_\n\n### Bazel version\n\n_No response_\n\n### GCC/Compiler version\n\n_No response_\n\n### CUDA/cuDNN version\n\n_No response_\n\n### GPU model and memory\n\n_No response_\n\n### Current Behaviour?\n\nshell\nHow to calculate 45 degree standing position of body from camera in swift.\n\n\n\n### Standalone code to reproduce the issue\n\nshell\nHow to calculate 45 degree standing position of body from camera in swift using the body keypoints. (Pose estimation)\n\n\n\n### Relevant log output\n\n_No response_</details>"</li></ul> |
| bug | <ul><li>'Abort when running tensorflow.python.ops.gen_array_ops.depth_to_space ### Issue type\n\nBug\n\n### Have you reproduced the bug with TensorFlow Nightly?\n\nNo\n\n### Source\n\nbinary\n\n### TensorFlow version\n\n2.11.0\n\n### Custom code\n\nYes\n\n### OS platform and distribution\n\n22.04\n\n### Mobile device\n\n_No response_\n\n### Python version\n\n3.9\n\n### Bazel version\n\n_No response_\n\n### GCC/compiler version\n\n_No response_\n\n### CUDA/cuDNN version\n\nnvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0\n\n### GPU model and memory\n\n_No response_\n\n### Current behavior?\n\nDue to very large integer argument\n\n### Standalone code to reproduce the issue\n\nshell\nimport tensorflow as tf\r\nimport os\r\nimport numpy as np\r\nfrom tensorflow.python.ops import gen_array_ops\r\ntry:\r\n arg_0_tensor = tf.random.uniform([3, 2, 3, 4], dtype=tf.float32)\r\n arg_0 = tf.identity(arg_0_tensor)\r\n arg_1 = 2147483647\r\n arg_2 = "NHWC"\r\n out = gen_array_ops.depth_to_space(arg_0,arg_1,arg_2,)\r\nexcept Exception as e:\r\n print("Error:"+str(e))\r\n\r\n\n\n\n\n### Relevant log output\n\nshell\n023-08-13 00:23:53.644564: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2023-08-13 00:23:54.491071: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.510564: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.510736: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.511051: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-13 00:23:54.511595: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.511717: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.511830: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.572398: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.572634: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.572791: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.572916: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 153 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5\r\n2023-08-13 00:23:54.594062: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:735] failed to allocate 153.88M (161349632 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory\r\n2023-08-13 00:23:54.594484: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:735] failed to allocate 138.49M (145214720 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory\r\n2023-08-13 00:23:54.600623: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Expected a non-negative size, got -2\r\nAborted\r\n\r\n\n\n'</li><li>"float8 (both e4m3fn and e5m2) missing from numbertype ### Issue Type\r\n\r\nBug\r\n\r\n### Have you reproduced the bug with TF nightly?\r\n\r\nNo\r\n\r\n### Source\r\n\r\nbinary\r\n\r\n### Tensorflow Version\r\n\r\n2.12.0\r\n\r\n### Custom Code\r\n\r\nYes\r\n\r\n### OS Platform and Distribution\r\n\r\nmacOS-13.2.1-arm64-arm-64bit\r\n\r\n### Mobile device\r\n\r\n_No response_\r\n\r\n### Python version\r\n\r\n3.9.6\r\n\r\n### Bazel version\r\n\r\n_No response_\r\n\r\n### GCC/Compiler version\r\n\r\n_No response_\r\n\r\n### CUDA/cuDNN version\r\n\r\n_No response_\r\n\r\n### GPU model and memory\r\n\r\n_No response_\r\n\r\n### Current Behaviour?\r\n\r\nFP8 datatypes are missing from kNumberTypes in tensorflow/core/framework/types.h, and also missing from TF_CALL_FLOAT_TYPES(m) in tensorflow/core/framework/register_types.h. This causes simple ops (like slice, transpose, split, etc.) to raise NotFoundError.\r\n\r\n### Standalone code to reproduce the issue\r\n\r\npython\r\nimport tensorflow as tf\r\nfrom tensorflow.python.framework import dtypes\r\n\r\na = tf.constant([[1.2345678, 2.3456789, 3.4567891], [4.5678912, 5.6789123, 6.7891234]], dtype=dtypes.float16)\r\nprint(a)\r\n\r\na_fp8 = tf.cast(a, dtypes.float8_e4m3fn)\r\nprint(a_fp8)\r\n\r\nb = a_fp8[1:2] # tensorflow.python.framework.errors_impl.NotFoundError\r\nb = tf.transpose(a_fp8, [1, 0]) # tensorflow.python.framework.errors_impl.NotFoundError\r\n\r\n\r\n\r\n### Relevant log output\r\n\r\n\r\ntensorflow.python.framework.errors_impl.NotFoundError: Could not find device for node: {{node StridedSlice}} = StridedSlice[Index=DT_INT32, T=DT_FLOAT8_E4M3FN, begin_mask=0, ellipsis_mask=0, end_mask=0, new_axis_mask=0, shrink_axis_mask=0]\r\nAll kernels registered for op StridedSlice:\r\n device='XLA_CPU_JIT'; Index in [DT_INT32, DT_INT16, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, 930109355527764061, DT_HALF, DT_UINT32, DT_UINT64, DT_FLOAT8_E5M2, DT_FLOAT8_E4M3FN]\r\n device='CPU'; T in [DT_UINT64]\r\n device='CPU'; T in [DT_INT64]\r\n device='CPU'; T in [DT_UINT32]\r\n device='CPU'; T in [DT_UINT16]\r\n device='CPU'; T in [DT_INT16]\r\n device='CPU'; T in [DT_UINT8]\r\n device='CPU'; T in [DT_INT8]\r\n device='CPU'; T in [DT_INT32]\r\n device='CPU'; T in [DT_HALF]\r\n device='CPU'; T in [DT_BFLOAT16]\r\n device='CPU'; T in [DT_FLOAT]\r\n device='CPU'; T in [DT_DOUBLE]\r\n device='CPU'; T in [DT_COMPLEX64]\r\n device='CPU'; T in [DT_COMPLEX128]\r\n device='CPU'; T in [DT_BOOL]\r\n device='CPU'; T in [DT_STRING]\r\n device='CPU'; T in [DT_RESOURCE]\r\n device='CPU'; T in [DT_VARIANT]\r\n device='CPU'; T in [DT_QINT8]\r\n device='CPU'; T in [DT_QUINT8]\r\n device='CPU'; T in [DT_QINT32]\r\n device='DEFAULT'; T in [DT_INT32]\r\n [Op:StridedSlice] name: strided_slice/\r\n\r\n\r\n\r\ntensorflow.python.framework.errors_impl.NotFoundError: Could not find device for node: {{node Transpose}} = Transpose[T=DT_FLOAT8_E4M3FN, Tperm=DT_INT32]\r\nAll kernels registered for op Transpose:\r\n device='XLA_CPU_JIT'; Tperm in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, 930109355527764061, DT_HALF, DT_UINT32, DT_UINT64, DT_FLOAT8_E5M2, DT_FLOAT8_E4M3FN]\r\n device='CPU'; T in [DT_UINT64]\r\n device='CPU'; T in [DT_INT64]\r\n device='CPU'; T in [DT_UINT32]\r\n device='CPU'; T in [DT_UINT16]\r\n device='CPU'; T in [DT_INT16]\r\n device='CPU'; T in [DT_UINT8]\r\n device='CPU'; T in [DT_INT8]\r\n device='CPU'; T in [DT_INT32]\r\n device='CPU'; T in [DT_HALF]\r\n device='CPU'; T in [DT_BFLOAT16]\r\n device='CPU'; T in [DT_FLOAT]\r\n device='CPU'; T in [DT_DOUBLE]\r\n device='CPU'; T in [DT_COMPLEX64]\r\n device='CPU'; T in [DT_COMPLEX128]\r\n device='CPU'; T in [DT_BOOL]\r\n device='CPU'; T in [DT_STRING]\r\n device='CPU'; T in [DT_RESOURCE]\r\n device='CPU'; T in [DT_VARIANT]\r\n [Op:Transpose]\r\n"</li><li>"My customized OP gives incorrect outputs on GPUs since tf-nightly 2.13.0.dev20230413 ### Issue type\n\nBug\n\n### Have you reproduced the bug with TensorFlow Nightly?\n\nYes\n\n### Source\n\nbinary\n\n### TensorFlow version\n\n2.13\n\n### Custom code\n\nYes\n\n### OS platform and distribution\n\nfedora 36\n\n### Mobile device\n\n_No response_\n\n### Python version\n\n3.11.4\n\n### Bazel version\n\n_No response_\n\n### GCC/compiler version\n\n_No response_\n\n### CUDA/cuDNN version\n\n_No response_\n\n### GPU model and memory\n\n_No response_\n\n### Current behavior?\n\nI have a complex program based on TensorFlow with several customized OPs. These OPs were created following https://www.tensorflow.org/guide/create_op. Yesterday TF 2.13.0 was released, but after I upgraded to 2.13.0, I found that one of my customized OP gives incorrect results on GPUs and still has the correct outputs on CPUs.\r\n\r\nThen I tested many tf-nightly versions and found that tf-nightly 2.13.0.dev20230412 works but tf-nightly 2.13.0.dev20230413 fails. So the situation is shown in the following table:\r\n |
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("Data init API for TFLite Swift <details><summary>Click to expand!</summary>
### Issue Type
Feature Request
### Source
source
### Tensorflow Version
2.8+
### Custom Code
No
### OS Platform and Distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
```shell
The current Swift API only has `init` functions from files on disk unlike the Java (Android) API which has a byte buffer initializer. It'd be convenient if the Swift API could initialize `Interpreters` from `Data`.
Standalone code to reproduce the issue
No code. This is a feature request
Relevant log output
No response</details>")
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:-----|
| Word count | 5 | 353.7433 | 6124 |
| Label | Training Sample Count |
|:---------|:----------------------|
| bug | 200 |
| feature | 200 |
| question | 200 |
### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0007 | 1 | 0.1719 | - |
| 0.0067 | 10 | 0.2869 | - |
| 0.0133 | 20 | 0.2513 | - |
| 0.02 | 30 | 0.1871 | - |
| 0.0267 | 40 | 0.2065 | - |
| 0.0333 | 50 | 0.2302 | - |
| 0.04 | 60 | 0.1645 | - |
| 0.0467 | 70 | 0.1887 | - |
| 0.0533 | 80 | 0.1376 | - |
| 0.06 | 90 | 0.1171 | - |
| 0.0667 | 100 | 0.1303 | - |
| 0.0733 | 110 | 0.121 | - |
| 0.08 | 120 | 0.1126 | - |
| 0.0867 | 130 | 0.1247 | - |
| 0.0933 | 140 | 0.1764 | - |
| 0.1 | 150 | 0.0401 | - |
| 0.1067 | 160 | 0.1571 | - |
| 0.1133 | 170 | 0.0186 | - |
| 0.12 | 180 | 0.0501 | - |
| 0.1267 | 190 | 0.1003 | - |
| 0.1333 | 200 | 0.0152 | - |
| 0.14 | 210 | 0.0784 | - |
| 0.1467 | 220 | 0.1423 | - |
| 0.1533 | 230 | 0.1313 | - |
| 0.16 | 240 | 0.0799 | - |
| 0.1667 | 250 | 0.0542 | - |
| 0.1733 | 260 | 0.0426 | - |
| 0.18 | 270 | 0.047 | - |
| 0.1867 | 280 | 0.0062 | - |
| 0.1933 | 290 | 0.0085 | - |
| 0.2 | 300 | 0.0625 | - |
| 0.2067 | 310 | 0.095 | - |
| 0.2133 | 320 | 0.0262 | - |
| 0.22 | 330 | 0.0029 | - |
| 0.2267 | 340 | 0.0097 | - |
| 0.2333 | 350 | 0.063 | - |
| 0.24 | 360 | 0.0059 | - |
| 0.2467 | 370 | 0.0016 | - |
| 0.2533 | 380 | 0.0025 | - |
| 0.26 | 390 | 0.0033 | - |
| 0.2667 | 400 | 0.0006 | - |
| 0.2733 | 410 | 0.0032 | - |
| 0.28 | 420 | 0.0045 | - |
| 0.2867 | 430 | 0.0013 | - |
| 0.2933 | 440 | 0.0011 | - |
| 0.3 | 450 | 0.001 | - |
| 0.3067 | 460 | 0.0044 | - |
| 0.3133 | 470 | 0.001 | - |
| 0.32 | 480 | 0.0009 | - |
| 0.3267 | 490 | 0.0004 | - |
| 0.3333 | 500 | 0.0006 | - |
| 0.34 | 510 | 0.001 | - |
| 0.3467 | 520 | 0.0003 | - |
| 0.3533 | 530 | 0.0008 | - |
| 0.36 | 540 | 0.0003 | - |
| 0.3667 | 550 | 0.0023 | - |
| 0.3733 | 560 | 0.0336 | - |
| 0.38 | 570 | 0.0004 | - |
| 0.3867 | 580 | 0.0003 | - |
| 0.3933 | 590 | 0.0006 | - |
| 0.4 | 600 | 0.0008 | - |
| 0.4067 | 610 | 0.0011 | - |
| 0.4133 | 620 | 0.0002 | - |
| 0.42 | 630 | 0.0004 | - |
| 0.4267 | 640 | 0.0005 | - |
| 0.4333 | 650 | 0.0601 | - |
| 0.44 | 660 | 0.0003 | - |
| 0.4467 | 670 | 0.0003 | - |
| 0.4533 | 680 | 0.0006 | - |
| 0.46 | 690 | 0.0005 | - |
| 0.4667 | 700 | 0.0003 | - |
| 0.4733 | 710 | 0.0006 | - |
| 0.48 | 720 | 0.0001 | - |
| 0.4867 | 730 | 0.0002 | - |
| 0.4933 | 740 | 0.0002 | - |
| 0.5 | 750 | 0.0002 | - |
| 0.5067 | 760 | 0.0002 | - |
| 0.5133 | 770 | 0.0016 | - |
| 0.52 | 780 | 0.0001 | - |
| 0.5267 | 790 | 0.0005 | - |
| 0.5333 | 800 | 0.0004 | - |
| 0.54 | 810 | 0.0039 | - |
| 0.5467 | 820 | 0.0031 | - |
| 0.5533 | 830 | 0.0008 | - |
| 0.56 | 840 | 0.0003 | - |
| 0.5667 | 850 | 0.0002 | - |
| 0.5733 | 860 | 0.0002 | - |
| 0.58 | 870 | 0.0002 | - |
| 0.5867 | 880 | 0.0001 | - |
| 0.5933 | 890 | 0.0004 | - |
| 0.6 | 900 | 0.0002 | - |
| 0.6067 | 910 | 0.0008 | - |
| 0.6133 | 920 | 0.0005 | - |
| 0.62 | 930 | 0.0005 | - |
| 0.6267 | 940 | 0.0002 | - |
| 0.6333 | 950 | 0.0001 | - |
| 0.64 | 960 | 0.0002 | - |
| 0.6467 | 970 | 0.0007 | - |
| 0.6533 | 980 | 0.0002 | - |
| 0.66 | 990 | 0.0002 | - |
| 0.6667 | 1000 | 0.0002 | - |
| 0.6733 | 1010 | 0.0002 | - |
| 0.68 | 1020 | 0.0002 | - |
| 0.6867 | 1030 | 0.0002 | - |
| 0.6933 | 1040 | 0.0004 | - |
| 0.7 | 1050 | 0.0076 | - |
| 0.7067 | 1060 | 0.0002 | - |
| 0.7133 | 1070 | 0.0002 | - |
| 0.72 | 1080 | 0.0001 | - |
| 0.7267 | 1090 | 0.0002 | - |
| 0.7333 | 1100 | 0.0001 | - |
| 0.74 | 1110 | 0.0365 | - |
| 0.7467 | 1120 | 0.0002 | - |
| 0.7533 | 1130 | 0.0002 | - |
| 0.76 | 1140 | 0.0003 | - |
| 0.7667 | 1150 | 0.0002 | - |
| 0.7733 | 1160 | 0.0002 | - |
| 0.78 | 1170 | 0.0004 | - |
| 0.7867 | 1180 | 0.0001 | - |
| 0.7933 | 1190 | 0.0001 | - |
| 0.8 | 1200 | 0.0001 | - |
| 0.8067 | 1210 | 0.0001 | - |
| 0.8133 | 1220 | 0.0002 | - |
| 0.82 | 1230 | 0.0002 | - |
| 0.8267 | 1240 | 0.0001 | - |
| 0.8333 | 1250 | 0.0001 | - |
| 0.84 | 1260 | 0.0002 | - |
| 0.8467 | 1270 | 0.0002 | - |
| 0.8533 | 1280 | 0.0 | - |
| 0.86 | 1290 | 0.0002 | - |
| 0.8667 | 1300 | 0.032 | - |
| 0.8733 | 1310 | 0.0001 | - |
| 0.88 | 1320 | 0.0001 | - |
| 0.8867 | 1330 | 0.0001 | - |
| 0.8933 | 1340 | 0.0003 | - |
| 0.9 | 1350 | 0.0001 | - |
| 0.9067 | 1360 | 0.0001 | - |
| 0.9133 | 1370 | 0.0001 | - |
| 0.92 | 1380 | 0.0001 | - |
| 0.9267 | 1390 | 0.0001 | - |
| 0.9333 | 1400 | 0.0001 | - |
| 0.94 | 1410 | 0.0001 | - |
| 0.9467 | 1420 | 0.0001 | - |
| 0.9533 | 1430 | 0.031 | - |
| 0.96 | 1440 | 0.0001 | - |
| 0.9667 | 1450 | 0.0003 | - |
| 0.9733 | 1460 | 0.0001 | - |
| 0.98 | 1470 | 0.0001 | - |
| 0.9867 | 1480 | 0.0001 | - |
| 0.9933 | 1490 | 0.0001 | - |
| 1.0 | 1500 | 0.0001 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.3.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
- Task
- Text Classification
- Library
- setfit
- Type
- mpnet
- Created
- Jun 24, 2024
- Updated
- Jun 24, 2024