Hugging Face's logo

nilcars
/
tensorflow_tensorflow_model

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Model card Files Community
Edit model card

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:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
question
  • "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\nimage\r\n\r\nBut I couldn't find an example code showing how to parse this output.\r\n\r\nI tried to look into https://github.com/tensorflow/tensorflow/issues/29054 and https://github.com/tensorflow/tensorflow/issues/40298 but the output of the model is different from the one provided here.\r\n\r\nDo you have any example code available in Java, Python, or even better in C++ to parse this model output?\n\n### Standalone code to reproduce the issue\n\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_"
  • 'Tensorflow Lite library is crashing in WASM library at 3rd inference
    Click to expand! \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 \r\n#include "tflite_model.h"\r\n#include \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 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
  • 'tf.keras.optimizers.experimental.AdamW only support constant weight_decay
    Click to expand! \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_
    '
  • '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\n**Describe 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'
  • "How to calculate 45 degree standing position of body from camera in swift (Pose estimation)
    Click to expand! \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_
    "
bug
  • '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'
  • "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"
  • "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")


<!--
### 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.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## 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}
}
Downloads last month
2
Safetensors
Model size
109M params
Tensor type
F32
·
Inference Examples
Text Classification
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for nilcars/tensorflow_tensorflow_model

Base model

sentence-transformers/all-mpnet-base-v2
Finetuned
(165)
this model