modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-05 00:41:53
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 539
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-05 00:41:31
| card
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|---|---|---|---|---|---|---|---|---|---|
yonghun/q-FrozenLake-v1-4x4-Slippery
|
yonghun
| 2023-08-07T06:59:50Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-07T06:46:59Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.17 +/- 0.38
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="yonghun/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
PrakhAI/AIPlane
|
PrakhAI
| 2023-08-07T06:56:58Z | 0 | 0 | null |
[
"arxiv:1710.10196",
"arxiv:1802.05957",
"region:us"
] | null | 2023-08-05T19:12:41Z |
---
datasets:
- cifar10
- https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/
---
GAN model trained on [CIFAR10 (Airplane)](https://www.tensorflow.org/datasets/catalog/cifar10) and [FGVC Aircraft](https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/) images. The model leverages [Progressive Growing](https://arxiv.org/pdf/1710.10196.pdf) with [Spectral Normalization](https://arxiv.org/pdf/1802.05957.pdf).
Try out this model [here](https://huggingface.co/spaces/PrakhAI/AIPlane).
| Generated Images | Real Images (for comparison) |
| -------- | --------- |
|  |  |
# Training Progression
<video width="50%" controls>
<source src="https://cdn-uploads.huggingface.co/production/uploads/649f9483d76ca0fe679011c2/qFlnTITZwS3DSTxLp0Oa8.mp4" type="video/mp4">
</video>
# Details
[Colab Notebook](https://colab.research.google.com/drive/1b4KFZOnLERwQW_3jQ8FMABepKEAcDIK7?usp=sharing)
The model generates 32 x 32 images of Airplanes. It is trained on an NVIDIA T4 Colab Runtime.
The Critic consists of Convolutional Layers (3x3 kernel) with strides for downsampling, and Leaky ReLU activation. The critic uses [Spectral Normalization](https://arxiv.org/pdf/1802.05957.pdf), with more details [here](#spectral-normalization).
The Generator uses Transposed Convolutions (2x2 kernel) with strides for upsampling, and ReLU activation. The generator uses the variant of pixel-level Local Response Normalization proposed in the [Progressive Growing](https://arxiv.org/pdf/1710.10196.pdf) paper.
# Spectral Normalization
Spectral Normalization is a technique suggested for training GANs in [this paper](https://arxiv.org/pdf/1802.05957.pdf).
It aims to make the Critic's (Discriminator's) outputs mathematically continuous w.r.t. the space of input images, avoiding exploding gradients.
Spectral Normalization works very well in practice to stabilize the training of the GAN, as demonstrated by the example below (comparison at equivalent points during training):
| Batch Normalization | Spectral Normalization |
| ----------- | ------------ |
|  |  |
# Progressive Growing
Progressive Growing of GAN resolutions is suggested to improve the Quality and Stability of GAN training, especially for higher resolution models (1024x1024).
For 32x32 images of Airplanes, even a short initial round of Progressive Growing provides significant improvement (comparison at equivalent points during training):
| Flat Growing | Progressive Growing |
| ----------- | ------------ |
|  |  |
The generator for this model generates 4x4, 8x8, 16x16 and 32x32 images, which form the inputs for the critic. Each resolution is associated with a 'weight' (α<sub>4</sub>, α<sub>8</sub>, α<sub>16</sub>, α<sub>32</sub>), which indicate the focus on the corresponding image resolution at any given time during the training.
At the beginning of the training, α<sub>4</sub>=1, α<sub>8</sub>=0, α<sub>16</sub>=0, α<sub>32</sub>=0, with the values being α<sub>4</sub>=0, α<sub>8</sub>=0, α<sub>16</sub>=0, α<sub>32</sub>=1 towards the end.
|
ThaumielSparrow/nnue-unet
|
ThaumielSparrow
| 2023-08-07T06:51:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-08-07T06:49:17Z |
# Efficiently-Updatable Neural Network (NNUE) Refactor of Classic U-Net Architecture for Membrane Segmentation
### Developed by Luzhou Zhang - Project still under development
🧠
## Setup
Clone into repository: `git clone https://github.com/ThaumielSparrow/cremi-nnue`
Install dependencies: `pip install -r requirements.txt`
Download CREMI training and test data [here](https://cremi.org/data/).
Modify runtime variables in `main.py` and `train.py` and run program: `python main.py`
Note: This project has only been tested and validated for Python 3.9.X and 3.10.X with frozen packages. It is likely that any Python version >3.7 supports it.
## Docs
I'm not writing documentation lol
|
TinToTin/ppo-LunarLander-v2
|
TinToTin
| 2023-08-07T06:49:56Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-07T06:49:37Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 264.06 +/- 23.53
name: mean_reward
verified: false
---
# **ppo** Agent playing **LunarLander-v2**
This is a trained model of a **ppo** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
yonghun/q-FrozenLake-v1-4x4-noSlippery
|
yonghun
| 2023-08-07T06:42:48Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-07T06:42:46Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="yonghun/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
jaswant50/distilbert-base-uncased-jaswant-base-finetuned
|
jaswant50
| 2023-08-07T06:41:04Z | 0 | 0 |
transformers
|
[
"transformers",
"text-classification",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-31T17:12:25Z |
---
library_name: transformers
pipeline_tag: text-classification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mkly/TinyStories-1M-ONNX
|
mkly
| 2023-08-07T06:39:59Z | 16 | 1 |
transformers.js
|
[
"transformers.js",
"onnx",
"gpt_neo",
"text-generation",
"en",
"license:unknown",
"region:us"
] |
text-generation
| 2023-08-07T01:54:33Z |
---
license: unknown
language:
- en
library_name: transformers.js
tags:
- gpt_neo
---
[ONNX](https://onnx.ai/) format
Generated from [roneneldan/TinyStories-1M](https://huggingface.co/roneneldan/TinyStories-1M)
For use with [Transformers.js](https://huggingface.co/docs/transformers.js)
```js
const pipe = await pipeline(
"text-generation",
"mkly/TinyStories-1M-ONNX",
);
const response = await pipe(
"Some example text",
{
max_new_tokens: 500,
temperature: 0.9,
},
);
console.log(response[0].generated_text);
```
|
AmelieSchreiber/esm2_t12_35M_LoRA_RNA_binding
|
AmelieSchreiber
| 2023-08-07T06:36:45Z | 7 | 1 |
peft
|
[
"peft",
"transformers",
"biology",
"esm",
"esm2",
"protein",
"protein language model",
"en",
"license:mit",
"region:us"
] | null | 2023-08-07T06:21:02Z |
---
library_name: peft
license: mit
language:
- en
tags:
- transformers
- biology
- esm
- esm2
- protein
- protein language model
---
# ESM-2 RNA Binding Site LoRA
This is a Parameter Efficient Fine Tuning (PEFT) Low Rank Adaptation ([LoRA](https://huggingface.co/docs/peft/task_guides/token-classification-lora)) of
the [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) model for the (binary) token classification task of
predicting RNA binding sites of proteins. The Github with the training script and conda env YAML can be
[found here](https://github.com/Amelie-Schreiber/esm2_LoRA_binding_sites/tree/main). You can also find a version of this model
that was fine-tuned without LoRA [here](https://huggingface.co/AmelieSchreiber/esm2_t6_8M_UR50D_rna_binding_site_predictor).
## Training procedure
This is a Low Rank Adaptation (LoRA) of `esm2_t6_8M_UR50D`,
trained on `166` protein sequences in the [RNA binding sites dataset](https://huggingface.co/datasets/AmelieSchreiber/data_of_protein-rna_binding_sites)
using a `75/25` train/test split. It achieves an evaluation loss of `0.18801096081733704`.
### Framework versions
- PEFT 0.4.0
## Using the Model
To use, try running:
```python
from transformers import AutoModelForTokenClassification, AutoTokenizer
from peft import PeftModel
import torch
# Path to the saved LoRA model
model_path = "AmelieSchreiber/esm2_t12_35M_LoRA_RNA_binding"
# ESM2 base model
base_model_path = "facebook/esm2_t12_35M_UR50D"
# Load the model
base_model = AutoModelForTokenClassification.from_pretrained(base_model_path)
loaded_model = PeftModel.from_pretrained(base_model, model_path)
# Ensure the model is in evaluation mode
loaded_model.eval()
# Load the tokenizer
loaded_tokenizer = AutoTokenizer.from_pretrained(base_model_path)
# Protein sequence for inference
protein_sequence = "MAVPETRPNHTIYINNLNEKIKKDELKKSLHAIFSRFGQILDILVSRSLKMRGQAFVIFKEVSSATNALRSMQGFPFYDKPMRIQYAKTDSDIIAKMKGT" # Replace with your actual sequence
# Tokenize the sequence
inputs = loaded_tokenizer(protein_sequence, return_tensors="pt", truncation=True, max_length=1024, padding='max_length')
# Run the model
with torch.no_grad():
logits = loaded_model(**inputs).logits
# Get predictions
tokens = loaded_tokenizer.convert_ids_to_tokens(inputs["input_ids"][0]) # Convert input ids back to tokens
predictions = torch.argmax(logits, dim=2)
# Define labels
id2label = {
0: "No binding site",
1: "Binding site"
}
# Print the predicted labels for each token
for token, prediction in zip(tokens, predictions[0].numpy()):
if token not in ['<pad>', '<cls>', '<eos>']:
print((token, id2label[prediction]))
```
|
TheRains/yt-special-batch4-base
|
TheRains
| 2023-08-07T06:19:37Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"dataset:yt",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-07T05:07:54Z |
---
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- yt
metrics:
- wer
model-index:
- name: Whisper Small Indonesian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: yt id
type: yt
metrics:
- name: Wer
type: wer
value: 66.04630049931912
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Indonesian
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the yt id dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0175
- Wer: 66.0463
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4446 | 0.09 | 1000 | 1.2313 | 91.5959 |
| 1.0599 | 0.17 | 2000 | 1.1312 | 106.3420 |
| 1.1851 | 0.26 | 3000 | 1.0801 | 77.3166 |
| 1.0325 | 0.34 | 4000 | 1.0380 | 71.8436 |
| 1.008 | 0.43 | 5000 | 1.0175 | 66.0463 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Dr-Tetsuo/llama2-qlora-finetunined-french
|
Dr-Tetsuo
| 2023-08-07T06:16:26Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-07T06:16:10Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
TheRains/yt-special-batch8-tiny
|
TheRains
| 2023-08-07T06:13:43Z | 87 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"dataset:yt",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-05T05:06:05Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- whisper-event
- generated_from_trainer
datasets:
- yt
metrics:
- wer
model-index:
- name: Whisper Small Indonesian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: yt id
type: yt
metrics:
- name: Wer
type: wer
value: 76.37636988522145
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Indonesian
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the yt id dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1651
- Wer: 76.3764
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4295 | 0.17 | 1000 | 1.4094 | 110.1550 |
| 1.3042 | 0.34 | 2000 | 1.2886 | 86.2914 |
| 1.2212 | 0.52 | 3000 | 1.2206 | 84.1191 |
| 1.1306 | 0.69 | 4000 | 1.1814 | 78.1532 |
| 1.1333 | 0.86 | 5000 | 1.1651 | 76.3764 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-open-SSEC-f1-v1
|
hw2942
| 2023-08-07T06:10:49Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-chinese",
"base_model:finetune:google-bert/bert-base-chinese",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-07T06:01:30Z |
---
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-chinese-wallstreetcn-morning-news-market-overview-open-SSEC-f1-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-wallstreetcn-morning-news-market-overview-open-SSEC-f1-v1
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3043
- F1: 0.4167
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 38 | 0.6797 | 0.0 |
| No log | 2.0 | 76 | 0.6726 | 0.1538 |
| No log | 3.0 | 114 | 0.6660 | 0.6154 |
| No log | 4.0 | 152 | 0.7310 | 0.4545 |
| No log | 5.0 | 190 | 0.8288 | 0.5926 |
| No log | 6.0 | 228 | 0.9843 | 0.4545 |
| No log | 7.0 | 266 | 1.4159 | 0.4545 |
| No log | 8.0 | 304 | 1.9705 | 0.4348 |
| No log | 9.0 | 342 | 2.2006 | 0.4167 |
| No log | 10.0 | 380 | 2.3043 | 0.4167 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
TheRains/yt-special-batch4-tiny
|
TheRains
| 2023-08-07T06:02:35Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"dataset:yt",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-07T05:07:55Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- whisper-event
- generated_from_trainer
datasets:
- yt
metrics:
- wer
model-index:
- name: Whisper Small Indonesian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: yt id
type: yt
metrics:
- name: Wer
type: wer
value: 85.86991764477013
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Indonesian
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the yt id dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2325
- Wer: 85.8699
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6736 | 0.09 | 1000 | 1.4867 | 126.2823 |
| 1.3354 | 0.17 | 2000 | 1.3584 | 103.0219 |
| 1.4841 | 0.26 | 3000 | 1.2936 | 86.3303 |
| 1.2807 | 0.34 | 4000 | 1.2523 | 85.9477 |
| 1.2095 | 0.43 | 5000 | 1.2325 | 85.8699 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Moonforeva/ppo-Huggy
|
Moonforeva
| 2023-08-07T05:53:25Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-07T05:53:15Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Moonforeva/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Rihong/q-Taxi-v3
|
Rihong
| 2023-08-07T05:31:08Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-07T05:31:05Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.68
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Rihong/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Rihong/q-FrozenLake-v1-4x4-noSlippery
|
Rihong
| 2023-08-07T05:23:24Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-07T05:23:22Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Rihong/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
vgarg/my-dataset-identification-model-v3
|
vgarg
| 2023-08-07T05:14:44Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-08-07T05:14:29Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# vgarg/my-dataset-identification-model-v3
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("vgarg/my-dataset-identification-model-v3")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```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}
}
```
|
intfloat/e5-large
|
intfloat
| 2023-08-07T04:59:49Z | 18,018 | 74 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"en",
"arxiv:2212.03533",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-12-26T06:03:12Z |
---
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: e5-large
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 77.68656716417911
- type: ap
value: 41.336896075573584
- type: f1
value: 71.788561468075
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 90.04965
- type: ap
value: 86.24637009569418
- type: f1
value: 90.03896671762645
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 43.016000000000005
- type: f1
value: 42.1942431880186
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.107000000000003
- type: map_at_10
value: 40.464
- type: map_at_100
value: 41.577999999999996
- type: map_at_1000
value: 41.588
- type: map_at_3
value: 35.301
- type: map_at_5
value: 38.263000000000005
- type: mrr_at_1
value: 25.605
- type: mrr_at_10
value: 40.64
- type: mrr_at_100
value: 41.760000000000005
- type: mrr_at_1000
value: 41.77
- type: mrr_at_3
value: 35.443000000000005
- type: mrr_at_5
value: 38.448
- type: ndcg_at_1
value: 25.107000000000003
- type: ndcg_at_10
value: 49.352000000000004
- type: ndcg_at_100
value: 53.98500000000001
- type: ndcg_at_1000
value: 54.208
- type: ndcg_at_3
value: 38.671
- type: ndcg_at_5
value: 43.991
- type: precision_at_1
value: 25.107000000000003
- type: precision_at_10
value: 7.795000000000001
- type: precision_at_100
value: 0.979
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 16.145
- type: precision_at_5
value: 12.262
- type: recall_at_1
value: 25.107000000000003
- type: recall_at_10
value: 77.952
- type: recall_at_100
value: 97.866
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 48.435
- type: recall_at_5
value: 61.309000000000005
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.19278045044154
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 41.37976387757665
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.07433334608074
- type: mrr
value: 73.44347711383723
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.4298072183543
- type: cos_sim_spearman
value: 84.73144873582848
- type: euclidean_pearson
value: 85.15885058870728
- type: euclidean_spearman
value: 85.42062106559356
- type: manhattan_pearson
value: 84.89409921792054
- type: manhattan_spearman
value: 85.31941394024344
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.14285714285714
- type: f1
value: 84.11674412565644
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.600076342340785
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 35.08861812135148
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.684000000000005
- type: map_at_10
value: 41.675000000000004
- type: map_at_100
value: 42.963
- type: map_at_1000
value: 43.078
- type: map_at_3
value: 38.708999999999996
- type: map_at_5
value: 40.316
- type: mrr_at_1
value: 39.485
- type: mrr_at_10
value: 47.152
- type: mrr_at_100
value: 47.96
- type: mrr_at_1000
value: 48.010000000000005
- type: mrr_at_3
value: 44.754
- type: mrr_at_5
value: 46.285
- type: ndcg_at_1
value: 39.485
- type: ndcg_at_10
value: 46.849000000000004
- type: ndcg_at_100
value: 52.059
- type: ndcg_at_1000
value: 54.358
- type: ndcg_at_3
value: 42.705
- type: ndcg_at_5
value: 44.663000000000004
- type: precision_at_1
value: 39.485
- type: precision_at_10
value: 8.455
- type: precision_at_100
value: 1.3379999999999999
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 19.695
- type: precision_at_5
value: 13.905999999999999
- type: recall_at_1
value: 32.684000000000005
- type: recall_at_10
value: 56.227000000000004
- type: recall_at_100
value: 78.499
- type: recall_at_1000
value: 94.021
- type: recall_at_3
value: 44.157999999999994
- type: recall_at_5
value: 49.694
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.875999999999998
- type: map_at_10
value: 41.603
- type: map_at_100
value: 42.825
- type: map_at_1000
value: 42.961
- type: map_at_3
value: 38.655
- type: map_at_5
value: 40.294999999999995
- type: mrr_at_1
value: 40.127
- type: mrr_at_10
value: 47.959
- type: mrr_at_100
value: 48.59
- type: mrr_at_1000
value: 48.634
- type: mrr_at_3
value: 45.786
- type: mrr_at_5
value: 46.964
- type: ndcg_at_1
value: 40.127
- type: ndcg_at_10
value: 47.176
- type: ndcg_at_100
value: 51.346000000000004
- type: ndcg_at_1000
value: 53.502
- type: ndcg_at_3
value: 43.139
- type: ndcg_at_5
value: 44.883
- type: precision_at_1
value: 40.127
- type: precision_at_10
value: 8.72
- type: precision_at_100
value: 1.387
- type: precision_at_1000
value: 0.188
- type: precision_at_3
value: 20.637
- type: precision_at_5
value: 14.446
- type: recall_at_1
value: 31.875999999999998
- type: recall_at_10
value: 56.54900000000001
- type: recall_at_100
value: 73.939
- type: recall_at_1000
value: 87.732
- type: recall_at_3
value: 44.326
- type: recall_at_5
value: 49.445
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 41.677
- type: map_at_10
value: 52.222
- type: map_at_100
value: 53.229000000000006
- type: map_at_1000
value: 53.288000000000004
- type: map_at_3
value: 49.201
- type: map_at_5
value: 51.00599999999999
- type: mrr_at_1
value: 47.524
- type: mrr_at_10
value: 55.745999999999995
- type: mrr_at_100
value: 56.433
- type: mrr_at_1000
value: 56.464999999999996
- type: mrr_at_3
value: 53.37499999999999
- type: mrr_at_5
value: 54.858
- type: ndcg_at_1
value: 47.524
- type: ndcg_at_10
value: 57.406
- type: ndcg_at_100
value: 61.403
- type: ndcg_at_1000
value: 62.7
- type: ndcg_at_3
value: 52.298
- type: ndcg_at_5
value: 55.02
- type: precision_at_1
value: 47.524
- type: precision_at_10
value: 8.865
- type: precision_at_100
value: 1.179
- type: precision_at_1000
value: 0.134
- type: precision_at_3
value: 22.612
- type: precision_at_5
value: 15.461
- type: recall_at_1
value: 41.677
- type: recall_at_10
value: 69.346
- type: recall_at_100
value: 86.344
- type: recall_at_1000
value: 95.703
- type: recall_at_3
value: 55.789
- type: recall_at_5
value: 62.488
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.991999999999997
- type: map_at_10
value: 32.804
- type: map_at_100
value: 33.812999999999995
- type: map_at_1000
value: 33.897
- type: map_at_3
value: 30.567
- type: map_at_5
value: 31.599
- type: mrr_at_1
value: 27.797
- type: mrr_at_10
value: 34.768
- type: mrr_at_100
value: 35.702
- type: mrr_at_1000
value: 35.766
- type: mrr_at_3
value: 32.637
- type: mrr_at_5
value: 33.614
- type: ndcg_at_1
value: 27.797
- type: ndcg_at_10
value: 36.966
- type: ndcg_at_100
value: 41.972
- type: ndcg_at_1000
value: 44.139
- type: ndcg_at_3
value: 32.547
- type: ndcg_at_5
value: 34.258
- type: precision_at_1
value: 27.797
- type: precision_at_10
value: 5.514
- type: precision_at_100
value: 0.8340000000000001
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 13.333
- type: precision_at_5
value: 9.04
- type: recall_at_1
value: 25.991999999999997
- type: recall_at_10
value: 47.941
- type: recall_at_100
value: 71.039
- type: recall_at_1000
value: 87.32799999999999
- type: recall_at_3
value: 36.01
- type: recall_at_5
value: 40.056000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.533
- type: map_at_10
value: 24.336
- type: map_at_100
value: 25.445
- type: map_at_1000
value: 25.561
- type: map_at_3
value: 22.116
- type: map_at_5
value: 23.347
- type: mrr_at_1
value: 21.642
- type: mrr_at_10
value: 28.910999999999998
- type: mrr_at_100
value: 29.836000000000002
- type: mrr_at_1000
value: 29.907
- type: mrr_at_3
value: 26.638
- type: mrr_at_5
value: 27.857
- type: ndcg_at_1
value: 21.642
- type: ndcg_at_10
value: 28.949
- type: ndcg_at_100
value: 34.211000000000006
- type: ndcg_at_1000
value: 37.031
- type: ndcg_at_3
value: 24.788
- type: ndcg_at_5
value: 26.685
- type: precision_at_1
value: 21.642
- type: precision_at_10
value: 5.137
- type: precision_at_100
value: 0.893
- type: precision_at_1000
value: 0.127
- type: precision_at_3
value: 11.733
- type: precision_at_5
value: 8.383000000000001
- type: recall_at_1
value: 17.533
- type: recall_at_10
value: 38.839
- type: recall_at_100
value: 61.458999999999996
- type: recall_at_1000
value: 81.58
- type: recall_at_3
value: 27.328999999999997
- type: recall_at_5
value: 32.168
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.126
- type: map_at_10
value: 37.872
- type: map_at_100
value: 39.229
- type: map_at_1000
value: 39.353
- type: map_at_3
value: 34.93
- type: map_at_5
value: 36.59
- type: mrr_at_1
value: 34.071
- type: mrr_at_10
value: 43.056
- type: mrr_at_100
value: 43.944
- type: mrr_at_1000
value: 43.999
- type: mrr_at_3
value: 40.536
- type: mrr_at_5
value: 42.065999999999995
- type: ndcg_at_1
value: 34.071
- type: ndcg_at_10
value: 43.503
- type: ndcg_at_100
value: 49.120000000000005
- type: ndcg_at_1000
value: 51.410999999999994
- type: ndcg_at_3
value: 38.767
- type: ndcg_at_5
value: 41.075
- type: precision_at_1
value: 34.071
- type: precision_at_10
value: 7.843999999999999
- type: precision_at_100
value: 1.2489999999999999
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 18.223
- type: precision_at_5
value: 13.050999999999998
- type: recall_at_1
value: 28.126
- type: recall_at_10
value: 54.952
- type: recall_at_100
value: 78.375
- type: recall_at_1000
value: 93.29899999999999
- type: recall_at_3
value: 41.714
- type: recall_at_5
value: 47.635
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.957
- type: map_at_10
value: 34.749
- type: map_at_100
value: 35.929
- type: map_at_1000
value: 36.043
- type: map_at_3
value: 31.947
- type: map_at_5
value: 33.575
- type: mrr_at_1
value: 32.078
- type: mrr_at_10
value: 39.844
- type: mrr_at_100
value: 40.71
- type: mrr_at_1000
value: 40.77
- type: mrr_at_3
value: 37.386
- type: mrr_at_5
value: 38.83
- type: ndcg_at_1
value: 32.078
- type: ndcg_at_10
value: 39.97
- type: ndcg_at_100
value: 45.254
- type: ndcg_at_1000
value: 47.818
- type: ndcg_at_3
value: 35.453
- type: ndcg_at_5
value: 37.631
- type: precision_at_1
value: 32.078
- type: precision_at_10
value: 7.158
- type: precision_at_100
value: 1.126
- type: precision_at_1000
value: 0.153
- type: precision_at_3
value: 16.743
- type: precision_at_5
value: 11.872
- type: recall_at_1
value: 25.957
- type: recall_at_10
value: 50.583
- type: recall_at_100
value: 73.593
- type: recall_at_1000
value: 91.23599999999999
- type: recall_at_3
value: 37.651
- type: recall_at_5
value: 43.626
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.1505
- type: map_at_10
value: 34.844833333333334
- type: map_at_100
value: 35.95216666666667
- type: map_at_1000
value: 36.06675
- type: map_at_3
value: 32.41975
- type: map_at_5
value: 33.74233333333333
- type: mrr_at_1
value: 31.923666666666662
- type: mrr_at_10
value: 38.87983333333334
- type: mrr_at_100
value: 39.706250000000004
- type: mrr_at_1000
value: 39.76708333333333
- type: mrr_at_3
value: 36.72008333333333
- type: mrr_at_5
value: 37.96933333333334
- type: ndcg_at_1
value: 31.923666666666662
- type: ndcg_at_10
value: 39.44258333333334
- type: ndcg_at_100
value: 44.31475
- type: ndcg_at_1000
value: 46.75
- type: ndcg_at_3
value: 35.36299999999999
- type: ndcg_at_5
value: 37.242333333333335
- type: precision_at_1
value: 31.923666666666662
- type: precision_at_10
value: 6.643333333333333
- type: precision_at_100
value: 1.0612499999999998
- type: precision_at_1000
value: 0.14575
- type: precision_at_3
value: 15.875250000000001
- type: precision_at_5
value: 11.088916666666664
- type: recall_at_1
value: 27.1505
- type: recall_at_10
value: 49.06349999999999
- type: recall_at_100
value: 70.60841666666666
- type: recall_at_1000
value: 87.72049999999999
- type: recall_at_3
value: 37.60575000000001
- type: recall_at_5
value: 42.511166666666675
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.101000000000003
- type: map_at_10
value: 30.147000000000002
- type: map_at_100
value: 30.98
- type: map_at_1000
value: 31.080000000000002
- type: map_at_3
value: 28.571
- type: map_at_5
value: 29.319
- type: mrr_at_1
value: 27.761000000000003
- type: mrr_at_10
value: 32.716
- type: mrr_at_100
value: 33.504
- type: mrr_at_1000
value: 33.574
- type: mrr_at_3
value: 31.135
- type: mrr_at_5
value: 32.032
- type: ndcg_at_1
value: 27.761000000000003
- type: ndcg_at_10
value: 33.358
- type: ndcg_at_100
value: 37.569
- type: ndcg_at_1000
value: 40.189
- type: ndcg_at_3
value: 30.291
- type: ndcg_at_5
value: 31.558000000000003
- type: precision_at_1
value: 27.761000000000003
- type: precision_at_10
value: 4.939
- type: precision_at_100
value: 0.759
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 12.577
- type: precision_at_5
value: 8.497
- type: recall_at_1
value: 25.101000000000003
- type: recall_at_10
value: 40.739
- type: recall_at_100
value: 60.089999999999996
- type: recall_at_1000
value: 79.768
- type: recall_at_3
value: 32.16
- type: recall_at_5
value: 35.131
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.112
- type: map_at_10
value: 26.119999999999997
- type: map_at_100
value: 27.031
- type: map_at_1000
value: 27.150000000000002
- type: map_at_3
value: 24.230999999999998
- type: map_at_5
value: 25.15
- type: mrr_at_1
value: 24.535
- type: mrr_at_10
value: 30.198000000000004
- type: mrr_at_100
value: 30.975
- type: mrr_at_1000
value: 31.051000000000002
- type: mrr_at_3
value: 28.338
- type: mrr_at_5
value: 29.269000000000002
- type: ndcg_at_1
value: 24.535
- type: ndcg_at_10
value: 30.147000000000002
- type: ndcg_at_100
value: 34.544000000000004
- type: ndcg_at_1000
value: 37.512
- type: ndcg_at_3
value: 26.726
- type: ndcg_at_5
value: 28.046
- type: precision_at_1
value: 24.535
- type: precision_at_10
value: 5.179
- type: precision_at_100
value: 0.859
- type: precision_at_1000
value: 0.128
- type: precision_at_3
value: 12.159
- type: precision_at_5
value: 8.424
- type: recall_at_1
value: 20.112
- type: recall_at_10
value: 38.312000000000005
- type: recall_at_100
value: 58.406000000000006
- type: recall_at_1000
value: 79.863
- type: recall_at_3
value: 28.358
- type: recall_at_5
value: 31.973000000000003
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.111
- type: map_at_10
value: 34.096
- type: map_at_100
value: 35.181000000000004
- type: map_at_1000
value: 35.276
- type: map_at_3
value: 31.745
- type: map_at_5
value: 33.045
- type: mrr_at_1
value: 31.343
- type: mrr_at_10
value: 37.994
- type: mrr_at_100
value: 38.873000000000005
- type: mrr_at_1000
value: 38.934999999999995
- type: mrr_at_3
value: 35.743
- type: mrr_at_5
value: 37.077
- type: ndcg_at_1
value: 31.343
- type: ndcg_at_10
value: 38.572
- type: ndcg_at_100
value: 43.854
- type: ndcg_at_1000
value: 46.190999999999995
- type: ndcg_at_3
value: 34.247
- type: ndcg_at_5
value: 36.28
- type: precision_at_1
value: 31.343
- type: precision_at_10
value: 6.166
- type: precision_at_100
value: 1
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 15.081
- type: precision_at_5
value: 10.428999999999998
- type: recall_at_1
value: 27.111
- type: recall_at_10
value: 48.422
- type: recall_at_100
value: 71.846
- type: recall_at_1000
value: 88.57000000000001
- type: recall_at_3
value: 36.435
- type: recall_at_5
value: 41.765
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.264
- type: map_at_10
value: 33.522
- type: map_at_100
value: 34.963
- type: map_at_1000
value: 35.175
- type: map_at_3
value: 31.366
- type: map_at_5
value: 32.621
- type: mrr_at_1
value: 31.028
- type: mrr_at_10
value: 37.230000000000004
- type: mrr_at_100
value: 38.149
- type: mrr_at_1000
value: 38.218
- type: mrr_at_3
value: 35.046
- type: mrr_at_5
value: 36.617
- type: ndcg_at_1
value: 31.028
- type: ndcg_at_10
value: 37.964999999999996
- type: ndcg_at_100
value: 43.342000000000006
- type: ndcg_at_1000
value: 46.471000000000004
- type: ndcg_at_3
value: 34.67
- type: ndcg_at_5
value: 36.458
- type: precision_at_1
value: 31.028
- type: precision_at_10
value: 6.937
- type: precision_at_100
value: 1.346
- type: precision_at_1000
value: 0.22799999999999998
- type: precision_at_3
value: 15.942
- type: precision_at_5
value: 11.462
- type: recall_at_1
value: 26.264
- type: recall_at_10
value: 45.571
- type: recall_at_100
value: 70.246
- type: recall_at_1000
value: 90.971
- type: recall_at_3
value: 36.276
- type: recall_at_5
value: 41.162
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.372999999999998
- type: map_at_10
value: 28.992
- type: map_at_100
value: 29.837999999999997
- type: map_at_1000
value: 29.939
- type: map_at_3
value: 26.999000000000002
- type: map_at_5
value: 28.044999999999998
- type: mrr_at_1
value: 25.692999999999998
- type: mrr_at_10
value: 30.984
- type: mrr_at_100
value: 31.799
- type: mrr_at_1000
value: 31.875999999999998
- type: mrr_at_3
value: 29.267
- type: mrr_at_5
value: 30.163
- type: ndcg_at_1
value: 25.692999999999998
- type: ndcg_at_10
value: 32.45
- type: ndcg_at_100
value: 37.103
- type: ndcg_at_1000
value: 39.678000000000004
- type: ndcg_at_3
value: 28.725
- type: ndcg_at_5
value: 30.351
- type: precision_at_1
value: 25.692999999999998
- type: precision_at_10
value: 4.806
- type: precision_at_100
value: 0.765
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 11.768
- type: precision_at_5
value: 8.096
- type: recall_at_1
value: 23.372999999999998
- type: recall_at_10
value: 41.281
- type: recall_at_100
value: 63.465
- type: recall_at_1000
value: 82.575
- type: recall_at_3
value: 31.063000000000002
- type: recall_at_5
value: 34.991
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.821
- type: map_at_10
value: 15.383
- type: map_at_100
value: 17.244999999999997
- type: map_at_1000
value: 17.445
- type: map_at_3
value: 12.64
- type: map_at_5
value: 13.941999999999998
- type: mrr_at_1
value: 19.544
- type: mrr_at_10
value: 29.738999999999997
- type: mrr_at_100
value: 30.923000000000002
- type: mrr_at_1000
value: 30.969
- type: mrr_at_3
value: 26.384
- type: mrr_at_5
value: 28.199
- type: ndcg_at_1
value: 19.544
- type: ndcg_at_10
value: 22.398
- type: ndcg_at_100
value: 30.253999999999998
- type: ndcg_at_1000
value: 33.876
- type: ndcg_at_3
value: 17.473
- type: ndcg_at_5
value: 19.154
- type: precision_at_1
value: 19.544
- type: precision_at_10
value: 7.217999999999999
- type: precision_at_100
value: 1.564
- type: precision_at_1000
value: 0.22300000000000003
- type: precision_at_3
value: 13.225000000000001
- type: precision_at_5
value: 10.319
- type: recall_at_1
value: 8.821
- type: recall_at_10
value: 28.110000000000003
- type: recall_at_100
value: 55.64
- type: recall_at_1000
value: 75.964
- type: recall_at_3
value: 16.195
- type: recall_at_5
value: 20.678
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.344
- type: map_at_10
value: 20.301
- type: map_at_100
value: 28.709
- type: map_at_1000
value: 30.470999999999997
- type: map_at_3
value: 14.584
- type: map_at_5
value: 16.930999999999997
- type: mrr_at_1
value: 67.25
- type: mrr_at_10
value: 75.393
- type: mrr_at_100
value: 75.742
- type: mrr_at_1000
value: 75.75
- type: mrr_at_3
value: 73.958
- type: mrr_at_5
value: 74.883
- type: ndcg_at_1
value: 56.00000000000001
- type: ndcg_at_10
value: 42.394
- type: ndcg_at_100
value: 47.091
- type: ndcg_at_1000
value: 54.215
- type: ndcg_at_3
value: 46.995
- type: ndcg_at_5
value: 44.214999999999996
- type: precision_at_1
value: 67.25
- type: precision_at_10
value: 33.525
- type: precision_at_100
value: 10.67
- type: precision_at_1000
value: 2.221
- type: precision_at_3
value: 49.417
- type: precision_at_5
value: 42.15
- type: recall_at_1
value: 9.344
- type: recall_at_10
value: 25.209
- type: recall_at_100
value: 52.329
- type: recall_at_1000
value: 74.2
- type: recall_at_3
value: 15.699
- type: recall_at_5
value: 19.24
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 48.05
- type: f1
value: 43.06718139212933
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 46.452
- type: map_at_10
value: 58.825
- type: map_at_100
value: 59.372
- type: map_at_1000
value: 59.399
- type: map_at_3
value: 56.264
- type: map_at_5
value: 57.879999999999995
- type: mrr_at_1
value: 49.82
- type: mrr_at_10
value: 62.178999999999995
- type: mrr_at_100
value: 62.641999999999996
- type: mrr_at_1000
value: 62.658
- type: mrr_at_3
value: 59.706
- type: mrr_at_5
value: 61.283
- type: ndcg_at_1
value: 49.82
- type: ndcg_at_10
value: 65.031
- type: ndcg_at_100
value: 67.413
- type: ndcg_at_1000
value: 68.014
- type: ndcg_at_3
value: 60.084
- type: ndcg_at_5
value: 62.858000000000004
- type: precision_at_1
value: 49.82
- type: precision_at_10
value: 8.876000000000001
- type: precision_at_100
value: 1.018
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 24.477
- type: precision_at_5
value: 16.208
- type: recall_at_1
value: 46.452
- type: recall_at_10
value: 80.808
- type: recall_at_100
value: 91.215
- type: recall_at_1000
value: 95.52000000000001
- type: recall_at_3
value: 67.62899999999999
- type: recall_at_5
value: 74.32900000000001
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.351
- type: map_at_10
value: 30.796
- type: map_at_100
value: 32.621
- type: map_at_1000
value: 32.799
- type: map_at_3
value: 26.491
- type: map_at_5
value: 28.933999999999997
- type: mrr_at_1
value: 36.265
- type: mrr_at_10
value: 45.556999999999995
- type: mrr_at_100
value: 46.323
- type: mrr_at_1000
value: 46.359
- type: mrr_at_3
value: 42.695
- type: mrr_at_5
value: 44.324000000000005
- type: ndcg_at_1
value: 36.265
- type: ndcg_at_10
value: 38.558
- type: ndcg_at_100
value: 45.18
- type: ndcg_at_1000
value: 48.292
- type: ndcg_at_3
value: 34.204
- type: ndcg_at_5
value: 35.735
- type: precision_at_1
value: 36.265
- type: precision_at_10
value: 10.879999999999999
- type: precision_at_100
value: 1.77
- type: precision_at_1000
value: 0.234
- type: precision_at_3
value: 23.044999999999998
- type: precision_at_5
value: 17.253
- type: recall_at_1
value: 18.351
- type: recall_at_10
value: 46.116
- type: recall_at_100
value: 70.786
- type: recall_at_1000
value: 89.46300000000001
- type: recall_at_3
value: 31.404
- type: recall_at_5
value: 37.678
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.847
- type: map_at_10
value: 54.269999999999996
- type: map_at_100
value: 55.152
- type: map_at_1000
value: 55.223
- type: map_at_3
value: 51.166
- type: map_at_5
value: 53.055
- type: mrr_at_1
value: 73.693
- type: mrr_at_10
value: 79.975
- type: mrr_at_100
value: 80.202
- type: mrr_at_1000
value: 80.214
- type: mrr_at_3
value: 78.938
- type: mrr_at_5
value: 79.595
- type: ndcg_at_1
value: 73.693
- type: ndcg_at_10
value: 63.334999999999994
- type: ndcg_at_100
value: 66.452
- type: ndcg_at_1000
value: 67.869
- type: ndcg_at_3
value: 58.829
- type: ndcg_at_5
value: 61.266
- type: precision_at_1
value: 73.693
- type: precision_at_10
value: 13.122
- type: precision_at_100
value: 1.5559999999999998
- type: precision_at_1000
value: 0.174
- type: precision_at_3
value: 37.083
- type: precision_at_5
value: 24.169999999999998
- type: recall_at_1
value: 36.847
- type: recall_at_10
value: 65.61099999999999
- type: recall_at_100
value: 77.792
- type: recall_at_1000
value: 87.17099999999999
- type: recall_at_3
value: 55.625
- type: recall_at_5
value: 60.425
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 82.1096
- type: ap
value: 76.67089212843918
- type: f1
value: 82.03535056754939
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 24.465
- type: map_at_10
value: 37.072
- type: map_at_100
value: 38.188
- type: map_at_1000
value: 38.232
- type: map_at_3
value: 33.134
- type: map_at_5
value: 35.453
- type: mrr_at_1
value: 25.142999999999997
- type: mrr_at_10
value: 37.669999999999995
- type: mrr_at_100
value: 38.725
- type: mrr_at_1000
value: 38.765
- type: mrr_at_3
value: 33.82
- type: mrr_at_5
value: 36.111
- type: ndcg_at_1
value: 25.142999999999997
- type: ndcg_at_10
value: 44.054
- type: ndcg_at_100
value: 49.364000000000004
- type: ndcg_at_1000
value: 50.456
- type: ndcg_at_3
value: 36.095
- type: ndcg_at_5
value: 40.23
- type: precision_at_1
value: 25.142999999999997
- type: precision_at_10
value: 6.845
- type: precision_at_100
value: 0.95
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 15.204999999999998
- type: precision_at_5
value: 11.221
- type: recall_at_1
value: 24.465
- type: recall_at_10
value: 65.495
- type: recall_at_100
value: 89.888
- type: recall_at_1000
value: 98.165
- type: recall_at_3
value: 43.964
- type: recall_at_5
value: 53.891
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.86228910168718
- type: f1
value: 93.69177113259104
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 76.3999088007296
- type: f1
value: 58.96668664333438
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.21788836583727
- type: f1
value: 71.4545936552952
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.39071956960323
- type: f1
value: 77.12398952847603
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 32.255379528166955
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 29.66423362872814
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.782211620375964
- type: mrr
value: 31.773479703044956
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.863
- type: map_at_10
value: 13.831
- type: map_at_100
value: 17.534
- type: map_at_1000
value: 19.012
- type: map_at_3
value: 10.143
- type: map_at_5
value: 12.034
- type: mrr_at_1
value: 46.749
- type: mrr_at_10
value: 55.376999999999995
- type: mrr_at_100
value: 56.009
- type: mrr_at_1000
value: 56.042
- type: mrr_at_3
value: 53.30200000000001
- type: mrr_at_5
value: 54.85
- type: ndcg_at_1
value: 44.582
- type: ndcg_at_10
value: 36.07
- type: ndcg_at_100
value: 33.39
- type: ndcg_at_1000
value: 41.884
- type: ndcg_at_3
value: 41.441
- type: ndcg_at_5
value: 39.861000000000004
- type: precision_at_1
value: 46.129999999999995
- type: precision_at_10
value: 26.594
- type: precision_at_100
value: 8.365
- type: precision_at_1000
value: 2.1260000000000003
- type: precision_at_3
value: 39.009
- type: precision_at_5
value: 34.861
- type: recall_at_1
value: 5.863
- type: recall_at_10
value: 17.961
- type: recall_at_100
value: 34.026
- type: recall_at_1000
value: 64.46499999999999
- type: recall_at_3
value: 11.242
- type: recall_at_5
value: 14.493
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.601
- type: map_at_10
value: 55.293000000000006
- type: map_at_100
value: 56.092
- type: map_at_1000
value: 56.111999999999995
- type: map_at_3
value: 51.269
- type: map_at_5
value: 53.787
- type: mrr_at_1
value: 43.221
- type: mrr_at_10
value: 57.882999999999996
- type: mrr_at_100
value: 58.408
- type: mrr_at_1000
value: 58.421
- type: mrr_at_3
value: 54.765
- type: mrr_at_5
value: 56.809
- type: ndcg_at_1
value: 43.221
- type: ndcg_at_10
value: 62.858999999999995
- type: ndcg_at_100
value: 65.987
- type: ndcg_at_1000
value: 66.404
- type: ndcg_at_3
value: 55.605000000000004
- type: ndcg_at_5
value: 59.723000000000006
- type: precision_at_1
value: 43.221
- type: precision_at_10
value: 9.907
- type: precision_at_100
value: 1.169
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 25.019000000000002
- type: precision_at_5
value: 17.474
- type: recall_at_1
value: 38.601
- type: recall_at_10
value: 82.966
- type: recall_at_100
value: 96.154
- type: recall_at_1000
value: 99.223
- type: recall_at_3
value: 64.603
- type: recall_at_5
value: 73.97200000000001
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.77
- type: map_at_10
value: 84.429
- type: map_at_100
value: 85.04599999999999
- type: map_at_1000
value: 85.065
- type: map_at_3
value: 81.461
- type: map_at_5
value: 83.316
- type: mrr_at_1
value: 81.51
- type: mrr_at_10
value: 87.52799999999999
- type: mrr_at_100
value: 87.631
- type: mrr_at_1000
value: 87.632
- type: mrr_at_3
value: 86.533
- type: mrr_at_5
value: 87.214
- type: ndcg_at_1
value: 81.47999999999999
- type: ndcg_at_10
value: 88.181
- type: ndcg_at_100
value: 89.39200000000001
- type: ndcg_at_1000
value: 89.52
- type: ndcg_at_3
value: 85.29299999999999
- type: ndcg_at_5
value: 86.88
- type: precision_at_1
value: 81.47999999999999
- type: precision_at_10
value: 13.367
- type: precision_at_100
value: 1.5230000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.227
- type: precision_at_5
value: 24.494
- type: recall_at_1
value: 70.77
- type: recall_at_10
value: 95.199
- type: recall_at_100
value: 99.37700000000001
- type: recall_at_1000
value: 99.973
- type: recall_at_3
value: 86.895
- type: recall_at_5
value: 91.396
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 50.686353396858344
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 61.3664675312921
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.7379999999999995
- type: map_at_10
value: 12.01
- type: map_at_100
value: 14.02
- type: map_at_1000
value: 14.310999999999998
- type: map_at_3
value: 8.459
- type: map_at_5
value: 10.281
- type: mrr_at_1
value: 23.3
- type: mrr_at_10
value: 34.108
- type: mrr_at_100
value: 35.217
- type: mrr_at_1000
value: 35.272
- type: mrr_at_3
value: 30.833
- type: mrr_at_5
value: 32.768
- type: ndcg_at_1
value: 23.3
- type: ndcg_at_10
value: 20.116999999999997
- type: ndcg_at_100
value: 27.961000000000002
- type: ndcg_at_1000
value: 33.149
- type: ndcg_at_3
value: 18.902
- type: ndcg_at_5
value: 16.742
- type: precision_at_1
value: 23.3
- type: precision_at_10
value: 10.47
- type: precision_at_100
value: 2.177
- type: precision_at_1000
value: 0.34299999999999997
- type: precision_at_3
value: 17.567
- type: precision_at_5
value: 14.78
- type: recall_at_1
value: 4.7379999999999995
- type: recall_at_10
value: 21.221999999999998
- type: recall_at_100
value: 44.242
- type: recall_at_1000
value: 69.652
- type: recall_at_3
value: 10.688
- type: recall_at_5
value: 14.982999999999999
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.84572946827069
- type: cos_sim_spearman
value: 80.48508130408966
- type: euclidean_pearson
value: 82.0481530027767
- type: euclidean_spearman
value: 80.45902876782752
- type: manhattan_pearson
value: 82.03728222483326
- type: manhattan_spearman
value: 80.45684282911755
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.33476464677516
- type: cos_sim_spearman
value: 75.93057758003266
- type: euclidean_pearson
value: 80.89685744015691
- type: euclidean_spearman
value: 76.29929953441706
- type: manhattan_pearson
value: 80.91391345459995
- type: manhattan_spearman
value: 76.31985463110914
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.63686106359005
- type: cos_sim_spearman
value: 85.22240034668202
- type: euclidean_pearson
value: 84.6074814189106
- type: euclidean_spearman
value: 85.17169644755828
- type: manhattan_pearson
value: 84.48329306239368
- type: manhattan_spearman
value: 85.0086508544768
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.95455774064745
- type: cos_sim_spearman
value: 80.54074646118492
- type: euclidean_pearson
value: 81.79598955554704
- type: euclidean_spearman
value: 80.55837617606814
- type: manhattan_pearson
value: 81.78213797905386
- type: manhattan_spearman
value: 80.5666746878273
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.92813309124739
- type: cos_sim_spearman
value: 88.81459873052108
- type: euclidean_pearson
value: 88.21193118930564
- type: euclidean_spearman
value: 88.87072745043731
- type: manhattan_pearson
value: 88.22576929706727
- type: manhattan_spearman
value: 88.8867671095791
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.6881529671839
- type: cos_sim_spearman
value: 85.2807092969554
- type: euclidean_pearson
value: 84.62334178652704
- type: euclidean_spearman
value: 85.2116373296784
- type: manhattan_pearson
value: 84.54948211541777
- type: manhattan_spearman
value: 85.10737722637882
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.55963694458408
- type: cos_sim_spearman
value: 89.36731628848683
- type: euclidean_pearson
value: 89.64975952985465
- type: euclidean_spearman
value: 89.29689484033007
- type: manhattan_pearson
value: 89.61234491713135
- type: manhattan_spearman
value: 89.20302520255782
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.411800961903886
- type: cos_sim_spearman
value: 62.99105515749963
- type: euclidean_pearson
value: 65.29826669549443
- type: euclidean_spearman
value: 63.29880964105775
- type: manhattan_pearson
value: 65.00126190601183
- type: manhattan_spearman
value: 63.32011025899179
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.83498531837608
- type: cos_sim_spearman
value: 87.21366640615442
- type: euclidean_pearson
value: 86.74764288798261
- type: euclidean_spearman
value: 87.06060470780834
- type: manhattan_pearson
value: 86.65971223951476
- type: manhattan_spearman
value: 86.99814399831457
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 83.94448463485881
- type: mrr
value: 95.36291867174221
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 59.928000000000004
- type: map_at_10
value: 68.577
- type: map_at_100
value: 69.35900000000001
- type: map_at_1000
value: 69.37299999999999
- type: map_at_3
value: 66.217
- type: map_at_5
value: 67.581
- type: mrr_at_1
value: 63
- type: mrr_at_10
value: 69.994
- type: mrr_at_100
value: 70.553
- type: mrr_at_1000
value: 70.56700000000001
- type: mrr_at_3
value: 68.167
- type: mrr_at_5
value: 69.11699999999999
- type: ndcg_at_1
value: 63
- type: ndcg_at_10
value: 72.58
- type: ndcg_at_100
value: 75.529
- type: ndcg_at_1000
value: 76.009
- type: ndcg_at_3
value: 68.523
- type: ndcg_at_5
value: 70.301
- type: precision_at_1
value: 63
- type: precision_at_10
value: 9.333
- type: precision_at_100
value: 1.09
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.444000000000003
- type: precision_at_5
value: 17.067
- type: recall_at_1
value: 59.928000000000004
- type: recall_at_10
value: 83.544
- type: recall_at_100
value: 96
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 72.072
- type: recall_at_5
value: 76.683
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.82178217821782
- type: cos_sim_ap
value: 95.41507679819003
- type: cos_sim_f1
value: 90.9456740442656
- type: cos_sim_precision
value: 91.49797570850203
- type: cos_sim_recall
value: 90.4
- type: dot_accuracy
value: 99.77227722772277
- type: dot_ap
value: 92.50123869445967
- type: dot_f1
value: 88.18414322250638
- type: dot_precision
value: 90.26178010471205
- type: dot_recall
value: 86.2
- type: euclidean_accuracy
value: 99.81782178217821
- type: euclidean_ap
value: 95.3935066749006
- type: euclidean_f1
value: 90.66128218071681
- type: euclidean_precision
value: 91.53924566768603
- type: euclidean_recall
value: 89.8
- type: manhattan_accuracy
value: 99.81881188118813
- type: manhattan_ap
value: 95.39767454613512
- type: manhattan_f1
value: 90.62019477191186
- type: manhattan_precision
value: 92.95478443743428
- type: manhattan_recall
value: 88.4
- type: max_accuracy
value: 99.82178217821782
- type: max_ap
value: 95.41507679819003
- type: max_f1
value: 90.9456740442656
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 64.96313921233748
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.602625720956745
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 51.32659230651731
- type: mrr
value: 52.33861726508785
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.01587644214203
- type: cos_sim_spearman
value: 30.974306908731013
- type: dot_pearson
value: 29.83339853838187
- type: dot_spearman
value: 30.07761671934048
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22
- type: map_at_10
value: 1.9539999999999997
- type: map_at_100
value: 11.437
- type: map_at_1000
value: 27.861000000000004
- type: map_at_3
value: 0.6479999999999999
- type: map_at_5
value: 1.0410000000000001
- type: mrr_at_1
value: 84
- type: mrr_at_10
value: 90.333
- type: mrr_at_100
value: 90.333
- type: mrr_at_1000
value: 90.333
- type: mrr_at_3
value: 90.333
- type: mrr_at_5
value: 90.333
- type: ndcg_at_1
value: 80
- type: ndcg_at_10
value: 78.31700000000001
- type: ndcg_at_100
value: 59.396
- type: ndcg_at_1000
value: 52.733
- type: ndcg_at_3
value: 81.46900000000001
- type: ndcg_at_5
value: 80.74
- type: precision_at_1
value: 84
- type: precision_at_10
value: 84
- type: precision_at_100
value: 60.980000000000004
- type: precision_at_1000
value: 23.432
- type: precision_at_3
value: 87.333
- type: precision_at_5
value: 86.8
- type: recall_at_1
value: 0.22
- type: recall_at_10
value: 2.156
- type: recall_at_100
value: 14.557999999999998
- type: recall_at_1000
value: 49.553999999999995
- type: recall_at_3
value: 0.685
- type: recall_at_5
value: 1.121
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.373
- type: map_at_10
value: 11.701
- type: map_at_100
value: 17.144000000000002
- type: map_at_1000
value: 18.624
- type: map_at_3
value: 6.552
- type: map_at_5
value: 9.372
- type: mrr_at_1
value: 38.775999999999996
- type: mrr_at_10
value: 51.975
- type: mrr_at_100
value: 52.873999999999995
- type: mrr_at_1000
value: 52.873999999999995
- type: mrr_at_3
value: 47.619
- type: mrr_at_5
value: 50.578
- type: ndcg_at_1
value: 36.735
- type: ndcg_at_10
value: 27.212999999999997
- type: ndcg_at_100
value: 37.245
- type: ndcg_at_1000
value: 48.602000000000004
- type: ndcg_at_3
value: 30.916
- type: ndcg_at_5
value: 30.799
- type: precision_at_1
value: 38.775999999999996
- type: precision_at_10
value: 23.469
- type: precision_at_100
value: 7.327
- type: precision_at_1000
value: 1.486
- type: precision_at_3
value: 31.973000000000003
- type: precision_at_5
value: 32.245000000000005
- type: recall_at_1
value: 3.373
- type: recall_at_10
value: 17.404
- type: recall_at_100
value: 46.105000000000004
- type: recall_at_1000
value: 80.35
- type: recall_at_3
value: 7.4399999999999995
- type: recall_at_5
value: 12.183
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.5592
- type: ap
value: 14.330910591410134
- type: f1
value: 54.45745186286521
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.20543293718167
- type: f1
value: 61.45365480309872
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 43.81162998944145
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.69011146212075
- type: cos_sim_ap
value: 76.09792353652536
- type: cos_sim_f1
value: 70.10202763786646
- type: cos_sim_precision
value: 68.65671641791045
- type: cos_sim_recall
value: 71.60949868073878
- type: dot_accuracy
value: 85.33110806461227
- type: dot_ap
value: 70.19304383327554
- type: dot_f1
value: 67.22494202525122
- type: dot_precision
value: 65.6847935548842
- type: dot_recall
value: 68.83905013192611
- type: euclidean_accuracy
value: 86.5410979316922
- type: euclidean_ap
value: 75.91906915651882
- type: euclidean_f1
value: 69.6798975672215
- type: euclidean_precision
value: 67.6865671641791
- type: euclidean_recall
value: 71.79419525065963
- type: manhattan_accuracy
value: 86.60070334386363
- type: manhattan_ap
value: 75.94617413885031
- type: manhattan_f1
value: 69.52689565780946
- type: manhattan_precision
value: 68.3312101910828
- type: manhattan_recall
value: 70.76517150395777
- type: max_accuracy
value: 86.69011146212075
- type: max_ap
value: 76.09792353652536
- type: max_f1
value: 70.10202763786646
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.25951798812434
- type: cos_sim_ap
value: 86.31476416599727
- type: cos_sim_f1
value: 78.52709971038477
- type: cos_sim_precision
value: 76.7629972792117
- type: cos_sim_recall
value: 80.37419156144134
- type: dot_accuracy
value: 88.03896456708192
- type: dot_ap
value: 83.26963599196237
- type: dot_f1
value: 76.72696459492317
- type: dot_precision
value: 73.56411162133521
- type: dot_recall
value: 80.17400677548507
- type: euclidean_accuracy
value: 89.21682772538519
- type: euclidean_ap
value: 86.29306071289969
- type: euclidean_f1
value: 78.40827030519554
- type: euclidean_precision
value: 77.42250243939053
- type: euclidean_recall
value: 79.41946412072683
- type: manhattan_accuracy
value: 89.22458959133776
- type: manhattan_ap
value: 86.2901934710645
- type: manhattan_f1
value: 78.54211378440453
- type: manhattan_precision
value: 76.85505858079729
- type: manhattan_recall
value: 80.30489682784109
- type: max_accuracy
value: 89.25951798812434
- type: max_ap
value: 86.31476416599727
- type: max_f1
value: 78.54211378440453
language:
- en
license: mit
---
## E5-large
**News (May 2023): please switch to [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2), which has better performance and same method of usage.**
[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
This model has 24 layers and the embedding size is 1024.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."]
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-large')
model = AutoModel.from_pretrained('intfloat/e5-large')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Training Details
Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
## Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/e5-large')
input_texts = [
'query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2022text,
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2212.03533},
year={2022}
}
```
## Limitations
This model only works for English texts. Long texts will be truncated to at most 512 tokens.
|
intfloat/e5-base
|
intfloat
| 2023-08-07T04:59:19Z | 325,938 | 20 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"en",
"arxiv:2212.03533",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-12-26T05:58:05Z |
---
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: e5-base
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 79.71641791044777
- type: ap
value: 44.15426065428253
- type: f1
value: 73.89474407693241
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 87.9649
- type: ap
value: 84.10171551915973
- type: f1
value: 87.94148377827356
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 42.645999999999994
- type: f1
value: 42.230574673549
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.814
- type: map_at_10
value: 42.681999999999995
- type: map_at_100
value: 43.714
- type: map_at_1000
value: 43.724000000000004
- type: map_at_3
value: 38.11
- type: map_at_5
value: 40.666999999999994
- type: mrr_at_1
value: 27.168999999999997
- type: mrr_at_10
value: 42.84
- type: mrr_at_100
value: 43.864
- type: mrr_at_1000
value: 43.875
- type: mrr_at_3
value: 38.193
- type: mrr_at_5
value: 40.793
- type: ndcg_at_1
value: 26.814
- type: ndcg_at_10
value: 51.410999999999994
- type: ndcg_at_100
value: 55.713
- type: ndcg_at_1000
value: 55.957
- type: ndcg_at_3
value: 41.955
- type: ndcg_at_5
value: 46.558
- type: precision_at_1
value: 26.814
- type: precision_at_10
value: 7.922999999999999
- type: precision_at_100
value: 0.9780000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 17.71
- type: precision_at_5
value: 12.859000000000002
- type: recall_at_1
value: 26.814
- type: recall_at_10
value: 79.232
- type: recall_at_100
value: 97.795
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 53.129000000000005
- type: recall_at_5
value: 64.29599999999999
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 44.56933066536439
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.47647746165173
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 59.65675531567043
- type: mrr
value: 72.95255683067317
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.83147014162338
- type: cos_sim_spearman
value: 85.1031439521441
- type: euclidean_pearson
value: 83.53609085510973
- type: euclidean_spearman
value: 84.59650590202833
- type: manhattan_pearson
value: 83.14611947586386
- type: manhattan_spearman
value: 84.13384475757064
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 83.32792207792208
- type: f1
value: 83.32037485050513
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 36.18605446588703
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 32.72379130181917
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.659
- type: map_at_10
value: 40.333999999999996
- type: map_at_100
value: 41.763
- type: map_at_1000
value: 41.894
- type: map_at_3
value: 37.561
- type: map_at_5
value: 39.084
- type: mrr_at_1
value: 37.482
- type: mrr_at_10
value: 45.736
- type: mrr_at_100
value: 46.591
- type: mrr_at_1000
value: 46.644999999999996
- type: mrr_at_3
value: 43.491
- type: mrr_at_5
value: 44.75
- type: ndcg_at_1
value: 37.482
- type: ndcg_at_10
value: 45.606
- type: ndcg_at_100
value: 51.172
- type: ndcg_at_1000
value: 53.407000000000004
- type: ndcg_at_3
value: 41.808
- type: ndcg_at_5
value: 43.449
- type: precision_at_1
value: 37.482
- type: precision_at_10
value: 8.254999999999999
- type: precision_at_100
value: 1.3719999999999999
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 19.695
- type: precision_at_5
value: 13.847999999999999
- type: recall_at_1
value: 30.659
- type: recall_at_10
value: 55.409
- type: recall_at_100
value: 78.687
- type: recall_at_1000
value: 93.068
- type: recall_at_3
value: 43.891999999999996
- type: recall_at_5
value: 48.678
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.977
- type: map_at_10
value: 40.296
- type: map_at_100
value: 41.453
- type: map_at_1000
value: 41.581
- type: map_at_3
value: 37.619
- type: map_at_5
value: 39.181
- type: mrr_at_1
value: 39.108
- type: mrr_at_10
value: 46.894000000000005
- type: mrr_at_100
value: 47.55
- type: mrr_at_1000
value: 47.598
- type: mrr_at_3
value: 44.766
- type: mrr_at_5
value: 46.062999999999995
- type: ndcg_at_1
value: 39.108
- type: ndcg_at_10
value: 45.717
- type: ndcg_at_100
value: 49.941
- type: ndcg_at_1000
value: 52.138
- type: ndcg_at_3
value: 42.05
- type: ndcg_at_5
value: 43.893
- type: precision_at_1
value: 39.108
- type: precision_at_10
value: 8.306
- type: precision_at_100
value: 1.3419999999999999
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 19.979
- type: precision_at_5
value: 14.038
- type: recall_at_1
value: 30.977
- type: recall_at_10
value: 54.688
- type: recall_at_100
value: 72.556
- type: recall_at_1000
value: 86.53800000000001
- type: recall_at_3
value: 43.388
- type: recall_at_5
value: 48.717
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.812
- type: map_at_10
value: 50.1
- type: map_at_100
value: 51.193999999999996
- type: map_at_1000
value: 51.258
- type: map_at_3
value: 47.510999999999996
- type: map_at_5
value: 48.891
- type: mrr_at_1
value: 45.266
- type: mrr_at_10
value: 53.459999999999994
- type: mrr_at_100
value: 54.19199999999999
- type: mrr_at_1000
value: 54.228
- type: mrr_at_3
value: 51.296
- type: mrr_at_5
value: 52.495999999999995
- type: ndcg_at_1
value: 45.266
- type: ndcg_at_10
value: 55.034000000000006
- type: ndcg_at_100
value: 59.458
- type: ndcg_at_1000
value: 60.862
- type: ndcg_at_3
value: 50.52799999999999
- type: ndcg_at_5
value: 52.564
- type: precision_at_1
value: 45.266
- type: precision_at_10
value: 8.483
- type: precision_at_100
value: 1.162
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 21.944
- type: precision_at_5
value: 14.721
- type: recall_at_1
value: 39.812
- type: recall_at_10
value: 66.36
- type: recall_at_100
value: 85.392
- type: recall_at_1000
value: 95.523
- type: recall_at_3
value: 54.127
- type: recall_at_5
value: 59.245000000000005
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.186
- type: map_at_10
value: 33.18
- type: map_at_100
value: 34.052
- type: map_at_1000
value: 34.149
- type: map_at_3
value: 31.029
- type: map_at_5
value: 32.321
- type: mrr_at_1
value: 28.136
- type: mrr_at_10
value: 35.195
- type: mrr_at_100
value: 35.996
- type: mrr_at_1000
value: 36.076
- type: mrr_at_3
value: 33.051
- type: mrr_at_5
value: 34.407
- type: ndcg_at_1
value: 28.136
- type: ndcg_at_10
value: 37.275999999999996
- type: ndcg_at_100
value: 41.935
- type: ndcg_at_1000
value: 44.389
- type: ndcg_at_3
value: 33.059
- type: ndcg_at_5
value: 35.313
- type: precision_at_1
value: 28.136
- type: precision_at_10
value: 5.457999999999999
- type: precision_at_100
value: 0.826
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 13.522
- type: precision_at_5
value: 9.424000000000001
- type: recall_at_1
value: 26.186
- type: recall_at_10
value: 47.961999999999996
- type: recall_at_100
value: 70.072
- type: recall_at_1000
value: 88.505
- type: recall_at_3
value: 36.752
- type: recall_at_5
value: 42.168
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.586000000000002
- type: map_at_10
value: 23.637
- type: map_at_100
value: 24.82
- type: map_at_1000
value: 24.95
- type: map_at_3
value: 21.428
- type: map_at_5
value: 22.555
- type: mrr_at_1
value: 20.771
- type: mrr_at_10
value: 27.839999999999996
- type: mrr_at_100
value: 28.887
- type: mrr_at_1000
value: 28.967
- type: mrr_at_3
value: 25.56
- type: mrr_at_5
value: 26.723000000000003
- type: ndcg_at_1
value: 20.771
- type: ndcg_at_10
value: 28.255000000000003
- type: ndcg_at_100
value: 33.886
- type: ndcg_at_1000
value: 36.963
- type: ndcg_at_3
value: 24.056
- type: ndcg_at_5
value: 25.818
- type: precision_at_1
value: 20.771
- type: precision_at_10
value: 5.1
- type: precision_at_100
value: 0.9119999999999999
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 11.526
- type: precision_at_5
value: 8.158999999999999
- type: recall_at_1
value: 16.586000000000002
- type: recall_at_10
value: 38.456
- type: recall_at_100
value: 62.666
- type: recall_at_1000
value: 84.47
- type: recall_at_3
value: 26.765
- type: recall_at_5
value: 31.297000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.831
- type: map_at_10
value: 37.545
- type: map_at_100
value: 38.934999999999995
- type: map_at_1000
value: 39.044000000000004
- type: map_at_3
value: 34.601
- type: map_at_5
value: 36.302
- type: mrr_at_1
value: 34.264
- type: mrr_at_10
value: 42.569
- type: mrr_at_100
value: 43.514
- type: mrr_at_1000
value: 43.561
- type: mrr_at_3
value: 40.167
- type: mrr_at_5
value: 41.678
- type: ndcg_at_1
value: 34.264
- type: ndcg_at_10
value: 42.914
- type: ndcg_at_100
value: 48.931999999999995
- type: ndcg_at_1000
value: 51.004000000000005
- type: ndcg_at_3
value: 38.096999999999994
- type: ndcg_at_5
value: 40.509
- type: precision_at_1
value: 34.264
- type: precision_at_10
value: 7.642
- type: precision_at_100
value: 1.258
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 17.453
- type: precision_at_5
value: 12.608
- type: recall_at_1
value: 28.831
- type: recall_at_10
value: 53.56999999999999
- type: recall_at_100
value: 79.26100000000001
- type: recall_at_1000
value: 92.862
- type: recall_at_3
value: 40.681
- type: recall_at_5
value: 46.597
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.461000000000002
- type: map_at_10
value: 35.885
- type: map_at_100
value: 37.039
- type: map_at_1000
value: 37.16
- type: map_at_3
value: 33.451
- type: map_at_5
value: 34.807
- type: mrr_at_1
value: 34.018
- type: mrr_at_10
value: 41.32
- type: mrr_at_100
value: 42.157
- type: mrr_at_1000
value: 42.223
- type: mrr_at_3
value: 39.288000000000004
- type: mrr_at_5
value: 40.481
- type: ndcg_at_1
value: 34.018
- type: ndcg_at_10
value: 40.821000000000005
- type: ndcg_at_100
value: 46.053
- type: ndcg_at_1000
value: 48.673
- type: ndcg_at_3
value: 36.839
- type: ndcg_at_5
value: 38.683
- type: precision_at_1
value: 34.018
- type: precision_at_10
value: 7.009
- type: precision_at_100
value: 1.123
- type: precision_at_1000
value: 0.153
- type: precision_at_3
value: 16.933
- type: precision_at_5
value: 11.826
- type: recall_at_1
value: 27.461000000000002
- type: recall_at_10
value: 50.285000000000004
- type: recall_at_100
value: 73.25500000000001
- type: recall_at_1000
value: 91.17699999999999
- type: recall_at_3
value: 39.104
- type: recall_at_5
value: 43.968
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.980083333333337
- type: map_at_10
value: 34.47208333333333
- type: map_at_100
value: 35.609249999999996
- type: map_at_1000
value: 35.72833333333333
- type: map_at_3
value: 32.189416666666666
- type: map_at_5
value: 33.44683333333334
- type: mrr_at_1
value: 31.731666666666662
- type: mrr_at_10
value: 38.518
- type: mrr_at_100
value: 39.38166666666667
- type: mrr_at_1000
value: 39.446999999999996
- type: mrr_at_3
value: 36.49966666666668
- type: mrr_at_5
value: 37.639916666666664
- type: ndcg_at_1
value: 31.731666666666662
- type: ndcg_at_10
value: 38.92033333333333
- type: ndcg_at_100
value: 44.01675
- type: ndcg_at_1000
value: 46.51075
- type: ndcg_at_3
value: 35.09766666666667
- type: ndcg_at_5
value: 36.842999999999996
- type: precision_at_1
value: 31.731666666666662
- type: precision_at_10
value: 6.472583333333332
- type: precision_at_100
value: 1.0665
- type: precision_at_1000
value: 0.14725000000000002
- type: precision_at_3
value: 15.659083333333331
- type: precision_at_5
value: 10.878833333333333
- type: recall_at_1
value: 26.980083333333337
- type: recall_at_10
value: 48.13925
- type: recall_at_100
value: 70.70149999999998
- type: recall_at_1000
value: 88.10775000000001
- type: recall_at_3
value: 37.30091666666667
- type: recall_at_5
value: 41.90358333333333
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.607999999999997
- type: map_at_10
value: 30.523
- type: map_at_100
value: 31.409
- type: map_at_1000
value: 31.507
- type: map_at_3
value: 28.915000000000003
- type: map_at_5
value: 29.756
- type: mrr_at_1
value: 28.681
- type: mrr_at_10
value: 33.409
- type: mrr_at_100
value: 34.241
- type: mrr_at_1000
value: 34.313
- type: mrr_at_3
value: 32.029999999999994
- type: mrr_at_5
value: 32.712
- type: ndcg_at_1
value: 28.681
- type: ndcg_at_10
value: 33.733000000000004
- type: ndcg_at_100
value: 38.32
- type: ndcg_at_1000
value: 40.937
- type: ndcg_at_3
value: 30.898999999999997
- type: ndcg_at_5
value: 32.088
- type: precision_at_1
value: 28.681
- type: precision_at_10
value: 4.968999999999999
- type: precision_at_100
value: 0.79
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 12.73
- type: precision_at_5
value: 8.558
- type: recall_at_1
value: 25.607999999999997
- type: recall_at_10
value: 40.722
- type: recall_at_100
value: 61.956999999999994
- type: recall_at_1000
value: 81.43
- type: recall_at_3
value: 32.785
- type: recall_at_5
value: 35.855
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.399
- type: map_at_10
value: 25.968000000000004
- type: map_at_100
value: 26.985999999999997
- type: map_at_1000
value: 27.105
- type: map_at_3
value: 24.215
- type: map_at_5
value: 25.157
- type: mrr_at_1
value: 24.708
- type: mrr_at_10
value: 29.971999999999998
- type: mrr_at_100
value: 30.858
- type: mrr_at_1000
value: 30.934
- type: mrr_at_3
value: 28.304000000000002
- type: mrr_at_5
value: 29.183999999999997
- type: ndcg_at_1
value: 24.708
- type: ndcg_at_10
value: 29.676000000000002
- type: ndcg_at_100
value: 34.656
- type: ndcg_at_1000
value: 37.588
- type: ndcg_at_3
value: 26.613
- type: ndcg_at_5
value: 27.919
- type: precision_at_1
value: 24.708
- type: precision_at_10
value: 5.01
- type: precision_at_100
value: 0.876
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 11.975
- type: precision_at_5
value: 8.279
- type: recall_at_1
value: 20.399
- type: recall_at_10
value: 36.935
- type: recall_at_100
value: 59.532
- type: recall_at_1000
value: 80.58
- type: recall_at_3
value: 27.979
- type: recall_at_5
value: 31.636999999999997
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.606
- type: map_at_10
value: 34.213
- type: map_at_100
value: 35.339999999999996
- type: map_at_1000
value: 35.458
- type: map_at_3
value: 31.987
- type: map_at_5
value: 33.322
- type: mrr_at_1
value: 31.53
- type: mrr_at_10
value: 37.911
- type: mrr_at_100
value: 38.879000000000005
- type: mrr_at_1000
value: 38.956
- type: mrr_at_3
value: 35.868
- type: mrr_at_5
value: 37.047999999999995
- type: ndcg_at_1
value: 31.53
- type: ndcg_at_10
value: 38.312000000000005
- type: ndcg_at_100
value: 43.812
- type: ndcg_at_1000
value: 46.414
- type: ndcg_at_3
value: 34.319
- type: ndcg_at_5
value: 36.312
- type: precision_at_1
value: 31.53
- type: precision_at_10
value: 5.970000000000001
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 14.738999999999999
- type: precision_at_5
value: 10.242999999999999
- type: recall_at_1
value: 27.606
- type: recall_at_10
value: 47.136
- type: recall_at_100
value: 71.253
- type: recall_at_1000
value: 89.39399999999999
- type: recall_at_3
value: 36.342
- type: recall_at_5
value: 41.388999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.855
- type: map_at_10
value: 31.963
- type: map_at_100
value: 33.371
- type: map_at_1000
value: 33.584
- type: map_at_3
value: 29.543999999999997
- type: map_at_5
value: 30.793
- type: mrr_at_1
value: 29.644
- type: mrr_at_10
value: 35.601
- type: mrr_at_100
value: 36.551
- type: mrr_at_1000
value: 36.623
- type: mrr_at_3
value: 33.399
- type: mrr_at_5
value: 34.575
- type: ndcg_at_1
value: 29.644
- type: ndcg_at_10
value: 36.521
- type: ndcg_at_100
value: 42.087
- type: ndcg_at_1000
value: 45.119
- type: ndcg_at_3
value: 32.797
- type: ndcg_at_5
value: 34.208
- type: precision_at_1
value: 29.644
- type: precision_at_10
value: 6.7
- type: precision_at_100
value: 1.374
- type: precision_at_1000
value: 0.22899999999999998
- type: precision_at_3
value: 15.152
- type: precision_at_5
value: 10.671999999999999
- type: recall_at_1
value: 24.855
- type: recall_at_10
value: 45.449
- type: recall_at_100
value: 70.921
- type: recall_at_1000
value: 90.629
- type: recall_at_3
value: 33.526
- type: recall_at_5
value: 37.848
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.781
- type: map_at_10
value: 30.020999999999997
- type: map_at_100
value: 30.948999999999998
- type: map_at_1000
value: 31.05
- type: map_at_3
value: 28.412
- type: map_at_5
value: 29.193
- type: mrr_at_1
value: 27.172
- type: mrr_at_10
value: 32.309
- type: mrr_at_100
value: 33.164
- type: mrr_at_1000
value: 33.239999999999995
- type: mrr_at_3
value: 30.775999999999996
- type: mrr_at_5
value: 31.562
- type: ndcg_at_1
value: 27.172
- type: ndcg_at_10
value: 33.178999999999995
- type: ndcg_at_100
value: 37.949
- type: ndcg_at_1000
value: 40.635
- type: ndcg_at_3
value: 30.107
- type: ndcg_at_5
value: 31.36
- type: precision_at_1
value: 27.172
- type: precision_at_10
value: 4.769
- type: precision_at_100
value: 0.769
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 12.261
- type: precision_at_5
value: 8.17
- type: recall_at_1
value: 24.781
- type: recall_at_10
value: 40.699000000000005
- type: recall_at_100
value: 62.866
- type: recall_at_1000
value: 83.11699999999999
- type: recall_at_3
value: 32.269999999999996
- type: recall_at_5
value: 35.443999999999996
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.2139999999999995
- type: map_at_10
value: 9.986
- type: map_at_100
value: 11.343
- type: map_at_1000
value: 11.55
- type: map_at_3
value: 7.961
- type: map_at_5
value: 8.967
- type: mrr_at_1
value: 12.052
- type: mrr_at_10
value: 20.165
- type: mrr_at_100
value: 21.317
- type: mrr_at_1000
value: 21.399
- type: mrr_at_3
value: 17.079
- type: mrr_at_5
value: 18.695
- type: ndcg_at_1
value: 12.052
- type: ndcg_at_10
value: 15.375
- type: ndcg_at_100
value: 21.858
- type: ndcg_at_1000
value: 26.145000000000003
- type: ndcg_at_3
value: 11.334
- type: ndcg_at_5
value: 12.798000000000002
- type: precision_at_1
value: 12.052
- type: precision_at_10
value: 5.16
- type: precision_at_100
value: 1.206
- type: precision_at_1000
value: 0.198
- type: precision_at_3
value: 8.73
- type: precision_at_5
value: 7.114
- type: recall_at_1
value: 5.2139999999999995
- type: recall_at_10
value: 20.669999999999998
- type: recall_at_100
value: 43.901
- type: recall_at_1000
value: 68.447
- type: recall_at_3
value: 11.049000000000001
- type: recall_at_5
value: 14.652999999999999
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.511000000000001
- type: map_at_10
value: 19.503
- type: map_at_100
value: 27.46
- type: map_at_1000
value: 29.187
- type: map_at_3
value: 14.030999999999999
- type: map_at_5
value: 16.329
- type: mrr_at_1
value: 63.74999999999999
- type: mrr_at_10
value: 73.419
- type: mrr_at_100
value: 73.691
- type: mrr_at_1000
value: 73.697
- type: mrr_at_3
value: 71.792
- type: mrr_at_5
value: 72.979
- type: ndcg_at_1
value: 53.125
- type: ndcg_at_10
value: 41.02
- type: ndcg_at_100
value: 45.407
- type: ndcg_at_1000
value: 52.68000000000001
- type: ndcg_at_3
value: 46.088
- type: ndcg_at_5
value: 43.236000000000004
- type: precision_at_1
value: 63.74999999999999
- type: precision_at_10
value: 32.35
- type: precision_at_100
value: 10.363
- type: precision_at_1000
value: 2.18
- type: precision_at_3
value: 49.667
- type: precision_at_5
value: 41.5
- type: recall_at_1
value: 8.511000000000001
- type: recall_at_10
value: 24.851
- type: recall_at_100
value: 50.745
- type: recall_at_1000
value: 73.265
- type: recall_at_3
value: 15.716
- type: recall_at_5
value: 19.256
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 49.43500000000001
- type: f1
value: 44.56288273966374
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.858
- type: map_at_10
value: 52.276
- type: map_at_100
value: 52.928
- type: map_at_1000
value: 52.966
- type: map_at_3
value: 49.729
- type: map_at_5
value: 51.27
- type: mrr_at_1
value: 43.624
- type: mrr_at_10
value: 55.22899999999999
- type: mrr_at_100
value: 55.823
- type: mrr_at_1000
value: 55.85
- type: mrr_at_3
value: 52.739999999999995
- type: mrr_at_5
value: 54.251000000000005
- type: ndcg_at_1
value: 43.624
- type: ndcg_at_10
value: 58.23500000000001
- type: ndcg_at_100
value: 61.315
- type: ndcg_at_1000
value: 62.20099999999999
- type: ndcg_at_3
value: 53.22
- type: ndcg_at_5
value: 55.88999999999999
- type: precision_at_1
value: 43.624
- type: precision_at_10
value: 8.068999999999999
- type: precision_at_100
value: 0.975
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 21.752
- type: precision_at_5
value: 14.515
- type: recall_at_1
value: 40.858
- type: recall_at_10
value: 73.744
- type: recall_at_100
value: 87.667
- type: recall_at_1000
value: 94.15599999999999
- type: recall_at_3
value: 60.287
- type: recall_at_5
value: 66.703
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.864
- type: map_at_10
value: 28.592000000000002
- type: map_at_100
value: 30.165
- type: map_at_1000
value: 30.364
- type: map_at_3
value: 24.586
- type: map_at_5
value: 26.717000000000002
- type: mrr_at_1
value: 35.031
- type: mrr_at_10
value: 43.876
- type: mrr_at_100
value: 44.683
- type: mrr_at_1000
value: 44.736
- type: mrr_at_3
value: 40.998000000000005
- type: mrr_at_5
value: 42.595
- type: ndcg_at_1
value: 35.031
- type: ndcg_at_10
value: 36.368
- type: ndcg_at_100
value: 42.472
- type: ndcg_at_1000
value: 45.973000000000006
- type: ndcg_at_3
value: 31.915
- type: ndcg_at_5
value: 33.394
- type: precision_at_1
value: 35.031
- type: precision_at_10
value: 10.139
- type: precision_at_100
value: 1.6420000000000001
- type: precision_at_1000
value: 0.22699999999999998
- type: precision_at_3
value: 21.142
- type: precision_at_5
value: 15.772
- type: recall_at_1
value: 17.864
- type: recall_at_10
value: 43.991
- type: recall_at_100
value: 66.796
- type: recall_at_1000
value: 87.64
- type: recall_at_3
value: 28.915999999999997
- type: recall_at_5
value: 35.185
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.556
- type: map_at_10
value: 53.056000000000004
- type: map_at_100
value: 53.909
- type: map_at_1000
value: 53.98
- type: map_at_3
value: 49.982
- type: map_at_5
value: 51.9
- type: mrr_at_1
value: 73.113
- type: mrr_at_10
value: 79.381
- type: mrr_at_100
value: 79.60300000000001
- type: mrr_at_1000
value: 79.617
- type: mrr_at_3
value: 78.298
- type: mrr_at_5
value: 78.995
- type: ndcg_at_1
value: 73.113
- type: ndcg_at_10
value: 62.21
- type: ndcg_at_100
value: 65.242
- type: ndcg_at_1000
value: 66.667
- type: ndcg_at_3
value: 57.717
- type: ndcg_at_5
value: 60.224
- type: precision_at_1
value: 73.113
- type: precision_at_10
value: 12.842999999999998
- type: precision_at_100
value: 1.522
- type: precision_at_1000
value: 0.17099999999999999
- type: precision_at_3
value: 36.178
- type: precision_at_5
value: 23.695
- type: recall_at_1
value: 36.556
- type: recall_at_10
value: 64.213
- type: recall_at_100
value: 76.077
- type: recall_at_1000
value: 85.53699999999999
- type: recall_at_3
value: 54.266999999999996
- type: recall_at_5
value: 59.236999999999995
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 75.958
- type: ap
value: 69.82869527654348
- type: f1
value: 75.89120903005633
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.608
- type: map_at_10
value: 36.144
- type: map_at_100
value: 37.244
- type: map_at_1000
value: 37.291999999999994
- type: map_at_3
value: 32.287
- type: map_at_5
value: 34.473
- type: mrr_at_1
value: 24.226
- type: mrr_at_10
value: 36.711
- type: mrr_at_100
value: 37.758
- type: mrr_at_1000
value: 37.8
- type: mrr_at_3
value: 32.92
- type: mrr_at_5
value: 35.104
- type: ndcg_at_1
value: 24.269
- type: ndcg_at_10
value: 43.138
- type: ndcg_at_100
value: 48.421
- type: ndcg_at_1000
value: 49.592000000000006
- type: ndcg_at_3
value: 35.269
- type: ndcg_at_5
value: 39.175
- type: precision_at_1
value: 24.269
- type: precision_at_10
value: 6.755999999999999
- type: precision_at_100
value: 0.941
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.938
- type: precision_at_5
value: 10.934000000000001
- type: recall_at_1
value: 23.608
- type: recall_at_10
value: 64.679
- type: recall_at_100
value: 89.027
- type: recall_at_1000
value: 97.91
- type: recall_at_3
value: 43.25
- type: recall_at_5
value: 52.617000000000004
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.21477428180576
- type: f1
value: 92.92502305092152
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 74.76744186046511
- type: f1
value: 59.19855520057899
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.24613315400134
- type: f1
value: 70.19950395651232
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.75857431069268
- type: f1
value: 76.5433450230191
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.525463791623604
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.28695907385136
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.068174046665224
- type: mrr
value: 30.827586642840803
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.322
- type: map_at_10
value: 13.919999999999998
- type: map_at_100
value: 17.416
- type: map_at_1000
value: 18.836
- type: map_at_3
value: 10.111
- type: map_at_5
value: 11.991999999999999
- type: mrr_at_1
value: 48.297000000000004
- type: mrr_at_10
value: 57.114
- type: mrr_at_100
value: 57.713
- type: mrr_at_1000
value: 57.751
- type: mrr_at_3
value: 55.108000000000004
- type: mrr_at_5
value: 56.533
- type: ndcg_at_1
value: 46.44
- type: ndcg_at_10
value: 36.589
- type: ndcg_at_100
value: 33.202
- type: ndcg_at_1000
value: 41.668
- type: ndcg_at_3
value: 41.302
- type: ndcg_at_5
value: 39.829
- type: precision_at_1
value: 47.988
- type: precision_at_10
value: 27.059
- type: precision_at_100
value: 8.235000000000001
- type: precision_at_1000
value: 2.091
- type: precision_at_3
value: 38.184000000000005
- type: precision_at_5
value: 34.365
- type: recall_at_1
value: 6.322
- type: recall_at_10
value: 18.288
- type: recall_at_100
value: 32.580999999999996
- type: recall_at_1000
value: 63.605999999999995
- type: recall_at_3
value: 11.266
- type: recall_at_5
value: 14.69
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.586999999999996
- type: map_at_10
value: 52.464
- type: map_at_100
value: 53.384
- type: map_at_1000
value: 53.405
- type: map_at_3
value: 48.408
- type: map_at_5
value: 50.788999999999994
- type: mrr_at_1
value: 40.904
- type: mrr_at_10
value: 54.974000000000004
- type: mrr_at_100
value: 55.60699999999999
- type: mrr_at_1000
value: 55.623
- type: mrr_at_3
value: 51.73799999999999
- type: mrr_at_5
value: 53.638
- type: ndcg_at_1
value: 40.904
- type: ndcg_at_10
value: 59.965999999999994
- type: ndcg_at_100
value: 63.613
- type: ndcg_at_1000
value: 64.064
- type: ndcg_at_3
value: 52.486
- type: ndcg_at_5
value: 56.377
- type: precision_at_1
value: 40.904
- type: precision_at_10
value: 9.551
- type: precision_at_100
value: 1.162
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 23.552
- type: precision_at_5
value: 16.436999999999998
- type: recall_at_1
value: 36.586999999999996
- type: recall_at_10
value: 80.094
- type: recall_at_100
value: 95.515
- type: recall_at_1000
value: 98.803
- type: recall_at_3
value: 60.907
- type: recall_at_5
value: 69.817
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.422
- type: map_at_10
value: 84.113
- type: map_at_100
value: 84.744
- type: map_at_1000
value: 84.762
- type: map_at_3
value: 81.171
- type: map_at_5
value: 83.039
- type: mrr_at_1
value: 81.12
- type: mrr_at_10
value: 87.277
- type: mrr_at_100
value: 87.384
- type: mrr_at_1000
value: 87.385
- type: mrr_at_3
value: 86.315
- type: mrr_at_5
value: 86.981
- type: ndcg_at_1
value: 81.12
- type: ndcg_at_10
value: 87.92
- type: ndcg_at_100
value: 89.178
- type: ndcg_at_1000
value: 89.29899999999999
- type: ndcg_at_3
value: 85.076
- type: ndcg_at_5
value: 86.67099999999999
- type: precision_at_1
value: 81.12
- type: precision_at_10
value: 13.325999999999999
- type: precision_at_100
value: 1.524
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.16
- type: precision_at_5
value: 24.456
- type: recall_at_1
value: 70.422
- type: recall_at_10
value: 95.00800000000001
- type: recall_at_100
value: 99.38
- type: recall_at_1000
value: 99.94800000000001
- type: recall_at_3
value: 86.809
- type: recall_at_5
value: 91.334
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 48.18491891699636
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 62.190639679711914
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.478
- type: map_at_10
value: 11.268
- type: map_at_100
value: 13.129
- type: map_at_1000
value: 13.41
- type: map_at_3
value: 8.103
- type: map_at_5
value: 9.609
- type: mrr_at_1
value: 22
- type: mrr_at_10
value: 32.248
- type: mrr_at_100
value: 33.355000000000004
- type: mrr_at_1000
value: 33.42
- type: mrr_at_3
value: 29.15
- type: mrr_at_5
value: 30.785
- type: ndcg_at_1
value: 22
- type: ndcg_at_10
value: 18.990000000000002
- type: ndcg_at_100
value: 26.302999999999997
- type: ndcg_at_1000
value: 31.537
- type: ndcg_at_3
value: 18.034
- type: ndcg_at_5
value: 15.655
- type: precision_at_1
value: 22
- type: precision_at_10
value: 9.91
- type: precision_at_100
value: 2.0420000000000003
- type: precision_at_1000
value: 0.33
- type: precision_at_3
value: 16.933
- type: precision_at_5
value: 13.719999999999999
- type: recall_at_1
value: 4.478
- type: recall_at_10
value: 20.087
- type: recall_at_100
value: 41.457
- type: recall_at_1000
value: 67.10199999999999
- type: recall_at_3
value: 10.313
- type: recall_at_5
value: 13.927999999999999
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.27341574565806
- type: cos_sim_spearman
value: 79.66419880841734
- type: euclidean_pearson
value: 81.32473321838208
- type: euclidean_spearman
value: 79.29828832085133
- type: manhattan_pearson
value: 81.25554065883132
- type: manhattan_spearman
value: 79.23275543279853
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.40468875905418
- type: cos_sim_spearman
value: 74.2189990321174
- type: euclidean_pearson
value: 80.74376966290956
- type: euclidean_spearman
value: 74.97663839079335
- type: manhattan_pearson
value: 80.69779331646207
- type: manhattan_spearman
value: 75.00225252917613
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 82.5745290053095
- type: cos_sim_spearman
value: 83.31401180333397
- type: euclidean_pearson
value: 82.96500607325534
- type: euclidean_spearman
value: 83.8534967935793
- type: manhattan_pearson
value: 82.83112050632508
- type: manhattan_spearman
value: 83.70877296557838
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 80.67833656607704
- type: cos_sim_spearman
value: 78.52252410630707
- type: euclidean_pearson
value: 80.071189514343
- type: euclidean_spearman
value: 78.95143545742796
- type: manhattan_pearson
value: 80.0128926165121
- type: manhattan_spearman
value: 78.91236678732628
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.48437639980746
- type: cos_sim_spearman
value: 88.34876527774259
- type: euclidean_pearson
value: 87.64898081823888
- type: euclidean_spearman
value: 88.58937180804213
- type: manhattan_pearson
value: 87.5942417815288
- type: manhattan_spearman
value: 88.53013922267687
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.69189187164781
- type: cos_sim_spearman
value: 84.15327883572112
- type: euclidean_pearson
value: 83.64202266685898
- type: euclidean_spearman
value: 84.6219602318862
- type: manhattan_pearson
value: 83.53256698709998
- type: manhattan_spearman
value: 84.49260712904946
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.09508017611589
- type: cos_sim_spearman
value: 87.23010990417097
- type: euclidean_pearson
value: 87.62545569077133
- type: euclidean_spearman
value: 86.71152051711714
- type: manhattan_pearson
value: 87.5057154278377
- type: manhattan_spearman
value: 86.60611898281267
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 61.72129893941176
- type: cos_sim_spearman
value: 62.87871412069194
- type: euclidean_pearson
value: 63.21077648290454
- type: euclidean_spearman
value: 63.03263080805978
- type: manhattan_pearson
value: 63.20740860135976
- type: manhattan_spearman
value: 62.89930471802817
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.039118236799
- type: cos_sim_spearman
value: 86.18102563389962
- type: euclidean_pearson
value: 85.62977041471879
- type: euclidean_spearman
value: 86.02478990544347
- type: manhattan_pearson
value: 85.60786740521806
- type: manhattan_spearman
value: 85.99546210442547
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 82.89875069737266
- type: mrr
value: 95.42621322033087
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 58.660999999999994
- type: map_at_10
value: 68.738
- type: map_at_100
value: 69.33200000000001
- type: map_at_1000
value: 69.352
- type: map_at_3
value: 66.502
- type: map_at_5
value: 67.686
- type: mrr_at_1
value: 61.667
- type: mrr_at_10
value: 70.003
- type: mrr_at_100
value: 70.441
- type: mrr_at_1000
value: 70.46
- type: mrr_at_3
value: 68.278
- type: mrr_at_5
value: 69.194
- type: ndcg_at_1
value: 61.667
- type: ndcg_at_10
value: 73.083
- type: ndcg_at_100
value: 75.56
- type: ndcg_at_1000
value: 76.01400000000001
- type: ndcg_at_3
value: 69.28699999999999
- type: ndcg_at_5
value: 70.85000000000001
- type: precision_at_1
value: 61.667
- type: precision_at_10
value: 9.6
- type: precision_at_100
value: 1.087
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 27.111
- type: precision_at_5
value: 17.467
- type: recall_at_1
value: 58.660999999999994
- type: recall_at_10
value: 85.02199999999999
- type: recall_at_100
value: 95.933
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 74.506
- type: recall_at_5
value: 78.583
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.8029702970297
- type: cos_sim_ap
value: 94.87673936635738
- type: cos_sim_f1
value: 90.00502260170768
- type: cos_sim_precision
value: 90.41372351160445
- type: cos_sim_recall
value: 89.60000000000001
- type: dot_accuracy
value: 99.57524752475247
- type: dot_ap
value: 84.81717934496321
- type: dot_f1
value: 78.23026646556059
- type: dot_precision
value: 78.66531850353893
- type: dot_recall
value: 77.8
- type: euclidean_accuracy
value: 99.8029702970297
- type: euclidean_ap
value: 94.74658253135284
- type: euclidean_f1
value: 90.08470353761834
- type: euclidean_precision
value: 89.77159880834161
- type: euclidean_recall
value: 90.4
- type: manhattan_accuracy
value: 99.8
- type: manhattan_ap
value: 94.69224030742787
- type: manhattan_f1
value: 89.9502487562189
- type: manhattan_precision
value: 89.50495049504951
- type: manhattan_recall
value: 90.4
- type: max_accuracy
value: 99.8029702970297
- type: max_ap
value: 94.87673936635738
- type: max_f1
value: 90.08470353761834
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 63.906039623153035
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.56053830923281
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.15326538775145
- type: mrr
value: 50.99279295051355
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.44030762047337
- type: cos_sim_spearman
value: 31.00910300264562
- type: dot_pearson
value: 26.88257194766013
- type: dot_spearman
value: 27.646202679013577
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.247
- type: map_at_10
value: 1.9429999999999998
- type: map_at_100
value: 10.82
- type: map_at_1000
value: 25.972
- type: map_at_3
value: 0.653
- type: map_at_5
value: 1.057
- type: mrr_at_1
value: 94
- type: mrr_at_10
value: 96.333
- type: mrr_at_100
value: 96.333
- type: mrr_at_1000
value: 96.333
- type: mrr_at_3
value: 96.333
- type: mrr_at_5
value: 96.333
- type: ndcg_at_1
value: 89
- type: ndcg_at_10
value: 79.63799999999999
- type: ndcg_at_100
value: 57.961
- type: ndcg_at_1000
value: 50.733
- type: ndcg_at_3
value: 84.224
- type: ndcg_at_5
value: 82.528
- type: precision_at_1
value: 94
- type: precision_at_10
value: 84.2
- type: precision_at_100
value: 59.36
- type: precision_at_1000
value: 22.738
- type: precision_at_3
value: 88
- type: precision_at_5
value: 86.8
- type: recall_at_1
value: 0.247
- type: recall_at_10
value: 2.131
- type: recall_at_100
value: 14.035
- type: recall_at_1000
value: 47.457
- type: recall_at_3
value: 0.6779999999999999
- type: recall_at_5
value: 1.124
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.603
- type: map_at_10
value: 11.667
- type: map_at_100
value: 16.474
- type: map_at_1000
value: 18.074
- type: map_at_3
value: 6.03
- type: map_at_5
value: 8.067
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 51.063
- type: mrr_at_100
value: 51.908
- type: mrr_at_1000
value: 51.908
- type: mrr_at_3
value: 47.959
- type: mrr_at_5
value: 49.694
- type: ndcg_at_1
value: 32.653
- type: ndcg_at_10
value: 28.305000000000003
- type: ndcg_at_100
value: 35.311
- type: ndcg_at_1000
value: 47.644999999999996
- type: ndcg_at_3
value: 32.187
- type: ndcg_at_5
value: 29.134999999999998
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 26.122
- type: precision_at_100
value: 6.755
- type: precision_at_1000
value: 1.467
- type: precision_at_3
value: 34.694
- type: precision_at_5
value: 30.203999999999997
- type: recall_at_1
value: 2.603
- type: recall_at_10
value: 18.716
- type: recall_at_100
value: 42.512
- type: recall_at_1000
value: 79.32000000000001
- type: recall_at_3
value: 7.59
- type: recall_at_5
value: 10.949
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 74.117
- type: ap
value: 15.89357321699319
- type: f1
value: 57.14385866369257
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.38370118845502
- type: f1
value: 61.67038693866553
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 42.57754941537969
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.1775049174465
- type: cos_sim_ap
value: 74.3994879581554
- type: cos_sim_f1
value: 69.32903671308551
- type: cos_sim_precision
value: 61.48193508879363
- type: cos_sim_recall
value: 79.47229551451187
- type: dot_accuracy
value: 81.65345413363534
- type: dot_ap
value: 59.690898346685096
- type: dot_f1
value: 57.27622826467499
- type: dot_precision
value: 51.34965473948525
- type: dot_recall
value: 64.74934036939314
- type: euclidean_accuracy
value: 86.04637301066937
- type: euclidean_ap
value: 74.33009001775268
- type: euclidean_f1
value: 69.2458374142997
- type: euclidean_precision
value: 64.59570580173595
- type: euclidean_recall
value: 74.6174142480211
- type: manhattan_accuracy
value: 86.11193896405793
- type: manhattan_ap
value: 74.2964140130421
- type: manhattan_f1
value: 69.11601528788066
- type: manhattan_precision
value: 64.86924323073363
- type: manhattan_recall
value: 73.95778364116094
- type: max_accuracy
value: 86.1775049174465
- type: max_ap
value: 74.3994879581554
- type: max_f1
value: 69.32903671308551
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.01501921061823
- type: cos_sim_ap
value: 85.97819287477351
- type: cos_sim_f1
value: 78.33882858518875
- type: cos_sim_precision
value: 75.49446626204926
- type: cos_sim_recall
value: 81.40591315060055
- type: dot_accuracy
value: 86.47494857763806
- type: dot_ap
value: 78.77420360340282
- type: dot_f1
value: 73.06433247936238
- type: dot_precision
value: 67.92140777983595
- type: dot_recall
value: 79.04989220819218
- type: euclidean_accuracy
value: 88.7297706368611
- type: euclidean_ap
value: 85.61550568529317
- type: euclidean_f1
value: 77.84805525263539
- type: euclidean_precision
value: 73.73639994491117
- type: euclidean_recall
value: 82.44533415460425
- type: manhattan_accuracy
value: 88.75111576823068
- type: manhattan_ap
value: 85.58701671476263
- type: manhattan_f1
value: 77.70169909067856
- type: manhattan_precision
value: 73.37666780704755
- type: manhattan_recall
value: 82.5685247921158
- type: max_accuracy
value: 89.01501921061823
- type: max_ap
value: 85.97819287477351
- type: max_f1
value: 78.33882858518875
language:
- en
license: mit
---
## E5-base
**News (May 2023): please switch to [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2), which has better performance and same method of usage.**
[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
This model has 12 layers and the embedding size is 768.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."]
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-base')
model = AutoModel.from_pretrained('intfloat/e5-base')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Training Details
Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
## Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/e5-base')
input_texts = [
'query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2022text,
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2212.03533},
year={2022}
}
```
## Limitations
This model only works for English texts. Long texts will be truncated to at most 512 tokens.
|
intfloat/e5-small
|
intfloat
| 2023-08-07T04:58:08Z | 56,836 | 41 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"en",
"arxiv:2212.03533",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-12-07T06:48:03Z |
---
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: e5-small
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.22388059701493
- type: ap
value: 40.27466219523129
- type: f1
value: 70.60533006025108
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 87.525775
- type: ap
value: 83.51063993897611
- type: f1
value: 87.49342736805572
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 42.611999999999995
- type: f1
value: 42.05088045932892
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.826
- type: map_at_10
value: 38.269
- type: map_at_100
value: 39.322
- type: map_at_1000
value: 39.344
- type: map_at_3
value: 33.428000000000004
- type: map_at_5
value: 36.063
- type: mrr_at_1
value: 24.253
- type: mrr_at_10
value: 38.425
- type: mrr_at_100
value: 39.478
- type: mrr_at_1000
value: 39.5
- type: mrr_at_3
value: 33.606
- type: mrr_at_5
value: 36.195
- type: ndcg_at_1
value: 23.826
- type: ndcg_at_10
value: 46.693
- type: ndcg_at_100
value: 51.469
- type: ndcg_at_1000
value: 52.002
- type: ndcg_at_3
value: 36.603
- type: ndcg_at_5
value: 41.365
- type: precision_at_1
value: 23.826
- type: precision_at_10
value: 7.383000000000001
- type: precision_at_100
value: 0.9530000000000001
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 15.268
- type: precision_at_5
value: 11.479000000000001
- type: recall_at_1
value: 23.826
- type: recall_at_10
value: 73.82600000000001
- type: recall_at_100
value: 95.306
- type: recall_at_1000
value: 99.431
- type: recall_at_3
value: 45.804
- type: recall_at_5
value: 57.397
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 44.13995374767436
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 37.13950072624313
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 59.35843292105327
- type: mrr
value: 73.72312359846987
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 84.55140418324174
- type: cos_sim_spearman
value: 84.21637675860022
- type: euclidean_pearson
value: 81.26069614610006
- type: euclidean_spearman
value: 83.25069210421785
- type: manhattan_pearson
value: 80.17441422581014
- type: manhattan_spearman
value: 81.87596198487877
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 81.87337662337661
- type: f1
value: 81.76647866926402
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.80600542614507
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 31.86321613256603
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.054
- type: map_at_10
value: 40.699999999999996
- type: map_at_100
value: 41.818
- type: map_at_1000
value: 41.959999999999994
- type: map_at_3
value: 37.742
- type: map_at_5
value: 39.427
- type: mrr_at_1
value: 38.769999999999996
- type: mrr_at_10
value: 46.150000000000006
- type: mrr_at_100
value: 46.865
- type: mrr_at_1000
value: 46.925
- type: mrr_at_3
value: 43.705
- type: mrr_at_5
value: 45.214999999999996
- type: ndcg_at_1
value: 38.769999999999996
- type: ndcg_at_10
value: 45.778
- type: ndcg_at_100
value: 50.38
- type: ndcg_at_1000
value: 52.922999999999995
- type: ndcg_at_3
value: 41.597
- type: ndcg_at_5
value: 43.631
- type: precision_at_1
value: 38.769999999999996
- type: precision_at_10
value: 8.269
- type: precision_at_100
value: 1.278
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 19.266
- type: precision_at_5
value: 13.705
- type: recall_at_1
value: 32.054
- type: recall_at_10
value: 54.947
- type: recall_at_100
value: 74.79599999999999
- type: recall_at_1000
value: 91.40899999999999
- type: recall_at_3
value: 42.431000000000004
- type: recall_at_5
value: 48.519
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.035
- type: map_at_10
value: 38.007000000000005
- type: map_at_100
value: 39.125
- type: map_at_1000
value: 39.251999999999995
- type: map_at_3
value: 35.77
- type: map_at_5
value: 37.057
- type: mrr_at_1
value: 36.497
- type: mrr_at_10
value: 44.077
- type: mrr_at_100
value: 44.743
- type: mrr_at_1000
value: 44.79
- type: mrr_at_3
value: 42.123
- type: mrr_at_5
value: 43.308
- type: ndcg_at_1
value: 36.497
- type: ndcg_at_10
value: 42.986000000000004
- type: ndcg_at_100
value: 47.323
- type: ndcg_at_1000
value: 49.624
- type: ndcg_at_3
value: 39.805
- type: ndcg_at_5
value: 41.286
- type: precision_at_1
value: 36.497
- type: precision_at_10
value: 7.8340000000000005
- type: precision_at_100
value: 1.269
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 19.023
- type: precision_at_5
value: 13.248
- type: recall_at_1
value: 29.035
- type: recall_at_10
value: 51.06
- type: recall_at_100
value: 69.64099999999999
- type: recall_at_1000
value: 84.49
- type: recall_at_3
value: 41.333999999999996
- type: recall_at_5
value: 45.663
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.239
- type: map_at_10
value: 47.873
- type: map_at_100
value: 48.842999999999996
- type: map_at_1000
value: 48.913000000000004
- type: map_at_3
value: 45.050000000000004
- type: map_at_5
value: 46.498
- type: mrr_at_1
value: 42.508
- type: mrr_at_10
value: 51.44
- type: mrr_at_100
value: 52.087
- type: mrr_at_1000
value: 52.129999999999995
- type: mrr_at_3
value: 49.164
- type: mrr_at_5
value: 50.343
- type: ndcg_at_1
value: 42.508
- type: ndcg_at_10
value: 53.31399999999999
- type: ndcg_at_100
value: 57.245000000000005
- type: ndcg_at_1000
value: 58.794000000000004
- type: ndcg_at_3
value: 48.295
- type: ndcg_at_5
value: 50.415
- type: precision_at_1
value: 42.508
- type: precision_at_10
value: 8.458
- type: precision_at_100
value: 1.133
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 21.191
- type: precision_at_5
value: 14.307
- type: recall_at_1
value: 37.239
- type: recall_at_10
value: 65.99000000000001
- type: recall_at_100
value: 82.99499999999999
- type: recall_at_1000
value: 94.128
- type: recall_at_3
value: 52.382
- type: recall_at_5
value: 57.648999999999994
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.039
- type: map_at_10
value: 29.694
- type: map_at_100
value: 30.587999999999997
- type: map_at_1000
value: 30.692999999999998
- type: map_at_3
value: 27.708
- type: map_at_5
value: 28.774
- type: mrr_at_1
value: 24.633
- type: mrr_at_10
value: 31.478
- type: mrr_at_100
value: 32.299
- type: mrr_at_1000
value: 32.381
- type: mrr_at_3
value: 29.435
- type: mrr_at_5
value: 30.446
- type: ndcg_at_1
value: 24.633
- type: ndcg_at_10
value: 33.697
- type: ndcg_at_100
value: 38.080000000000005
- type: ndcg_at_1000
value: 40.812
- type: ndcg_at_3
value: 29.654000000000003
- type: ndcg_at_5
value: 31.474000000000004
- type: precision_at_1
value: 24.633
- type: precision_at_10
value: 5.0729999999999995
- type: precision_at_100
value: 0.753
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 12.279
- type: precision_at_5
value: 8.452
- type: recall_at_1
value: 23.039
- type: recall_at_10
value: 44.275999999999996
- type: recall_at_100
value: 64.4
- type: recall_at_1000
value: 85.135
- type: recall_at_3
value: 33.394
- type: recall_at_5
value: 37.687
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.594999999999999
- type: map_at_10
value: 19.933999999999997
- type: map_at_100
value: 20.966
- type: map_at_1000
value: 21.087
- type: map_at_3
value: 17.749000000000002
- type: map_at_5
value: 19.156000000000002
- type: mrr_at_1
value: 17.662
- type: mrr_at_10
value: 24.407
- type: mrr_at_100
value: 25.385
- type: mrr_at_1000
value: 25.465
- type: mrr_at_3
value: 22.056
- type: mrr_at_5
value: 23.630000000000003
- type: ndcg_at_1
value: 17.662
- type: ndcg_at_10
value: 24.391
- type: ndcg_at_100
value: 29.681
- type: ndcg_at_1000
value: 32.923
- type: ndcg_at_3
value: 20.271
- type: ndcg_at_5
value: 22.621
- type: precision_at_1
value: 17.662
- type: precision_at_10
value: 4.44
- type: precision_at_100
value: 0.8200000000000001
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 9.577
- type: precision_at_5
value: 7.313
- type: recall_at_1
value: 13.594999999999999
- type: recall_at_10
value: 33.976
- type: recall_at_100
value: 57.43000000000001
- type: recall_at_1000
value: 80.958
- type: recall_at_3
value: 22.897000000000002
- type: recall_at_5
value: 28.714000000000002
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.683
- type: map_at_10
value: 35.068
- type: map_at_100
value: 36.311
- type: map_at_1000
value: 36.436
- type: map_at_3
value: 32.371
- type: map_at_5
value: 33.761
- type: mrr_at_1
value: 32.435
- type: mrr_at_10
value: 40.721000000000004
- type: mrr_at_100
value: 41.535
- type: mrr_at_1000
value: 41.593
- type: mrr_at_3
value: 38.401999999999994
- type: mrr_at_5
value: 39.567
- type: ndcg_at_1
value: 32.435
- type: ndcg_at_10
value: 40.538000000000004
- type: ndcg_at_100
value: 45.963
- type: ndcg_at_1000
value: 48.400999999999996
- type: ndcg_at_3
value: 36.048
- type: ndcg_at_5
value: 37.899
- type: precision_at_1
value: 32.435
- type: precision_at_10
value: 7.1129999999999995
- type: precision_at_100
value: 1.162
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 16.683
- type: precision_at_5
value: 11.684
- type: recall_at_1
value: 26.683
- type: recall_at_10
value: 51.517
- type: recall_at_100
value: 74.553
- type: recall_at_1000
value: 90.649
- type: recall_at_3
value: 38.495000000000005
- type: recall_at_5
value: 43.495
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.186
- type: map_at_10
value: 31.972
- type: map_at_100
value: 33.117000000000004
- type: map_at_1000
value: 33.243
- type: map_at_3
value: 29.423
- type: map_at_5
value: 30.847
- type: mrr_at_1
value: 29.794999999999998
- type: mrr_at_10
value: 36.767
- type: mrr_at_100
value: 37.645
- type: mrr_at_1000
value: 37.716
- type: mrr_at_3
value: 34.513
- type: mrr_at_5
value: 35.791000000000004
- type: ndcg_at_1
value: 29.794999999999998
- type: ndcg_at_10
value: 36.786
- type: ndcg_at_100
value: 41.94
- type: ndcg_at_1000
value: 44.830999999999996
- type: ndcg_at_3
value: 32.504
- type: ndcg_at_5
value: 34.404
- type: precision_at_1
value: 29.794999999999998
- type: precision_at_10
value: 6.518
- type: precision_at_100
value: 1.0659999999999998
- type: precision_at_1000
value: 0.149
- type: precision_at_3
value: 15.296999999999999
- type: precision_at_5
value: 10.731
- type: recall_at_1
value: 24.186
- type: recall_at_10
value: 46.617
- type: recall_at_100
value: 68.75
- type: recall_at_1000
value: 88.864
- type: recall_at_3
value: 34.199
- type: recall_at_5
value: 39.462
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.22083333333333
- type: map_at_10
value: 31.606666666666662
- type: map_at_100
value: 32.6195
- type: map_at_1000
value: 32.739999999999995
- type: map_at_3
value: 29.37825
- type: map_at_5
value: 30.596083333333336
- type: mrr_at_1
value: 28.607916666666668
- type: mrr_at_10
value: 35.54591666666666
- type: mrr_at_100
value: 36.33683333333333
- type: mrr_at_1000
value: 36.40624999999999
- type: mrr_at_3
value: 33.526250000000005
- type: mrr_at_5
value: 34.6605
- type: ndcg_at_1
value: 28.607916666666668
- type: ndcg_at_10
value: 36.07966666666667
- type: ndcg_at_100
value: 40.73308333333333
- type: ndcg_at_1000
value: 43.40666666666666
- type: ndcg_at_3
value: 32.23525
- type: ndcg_at_5
value: 33.97083333333333
- type: precision_at_1
value: 28.607916666666668
- type: precision_at_10
value: 6.120333333333335
- type: precision_at_100
value: 0.9921666666666668
- type: precision_at_1000
value: 0.14091666666666666
- type: precision_at_3
value: 14.54975
- type: precision_at_5
value: 10.153166666666667
- type: recall_at_1
value: 24.22083333333333
- type: recall_at_10
value: 45.49183333333334
- type: recall_at_100
value: 66.28133333333332
- type: recall_at_1000
value: 85.16541666666667
- type: recall_at_3
value: 34.6485
- type: recall_at_5
value: 39.229749999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.842
- type: map_at_10
value: 27.573999999999998
- type: map_at_100
value: 28.410999999999998
- type: map_at_1000
value: 28.502
- type: map_at_3
value: 25.921
- type: map_at_5
value: 26.888
- type: mrr_at_1
value: 24.08
- type: mrr_at_10
value: 29.915999999999997
- type: mrr_at_100
value: 30.669
- type: mrr_at_1000
value: 30.746000000000002
- type: mrr_at_3
value: 28.349000000000004
- type: mrr_at_5
value: 29.246
- type: ndcg_at_1
value: 24.08
- type: ndcg_at_10
value: 30.898999999999997
- type: ndcg_at_100
value: 35.272999999999996
- type: ndcg_at_1000
value: 37.679
- type: ndcg_at_3
value: 27.881
- type: ndcg_at_5
value: 29.432000000000002
- type: precision_at_1
value: 24.08
- type: precision_at_10
value: 4.678
- type: precision_at_100
value: 0.744
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 11.860999999999999
- type: precision_at_5
value: 8.16
- type: recall_at_1
value: 21.842
- type: recall_at_10
value: 38.66
- type: recall_at_100
value: 59.169000000000004
- type: recall_at_1000
value: 76.887
- type: recall_at_3
value: 30.532999999999998
- type: recall_at_5
value: 34.354
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.145
- type: map_at_10
value: 22.729
- type: map_at_100
value: 23.574
- type: map_at_1000
value: 23.695
- type: map_at_3
value: 21.044
- type: map_at_5
value: 21.981
- type: mrr_at_1
value: 20.888
- type: mrr_at_10
value: 26.529000000000003
- type: mrr_at_100
value: 27.308
- type: mrr_at_1000
value: 27.389000000000003
- type: mrr_at_3
value: 24.868000000000002
- type: mrr_at_5
value: 25.825
- type: ndcg_at_1
value: 20.888
- type: ndcg_at_10
value: 26.457000000000004
- type: ndcg_at_100
value: 30.764000000000003
- type: ndcg_at_1000
value: 33.825
- type: ndcg_at_3
value: 23.483999999999998
- type: ndcg_at_5
value: 24.836
- type: precision_at_1
value: 20.888
- type: precision_at_10
value: 4.58
- type: precision_at_100
value: 0.784
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 10.874
- type: precision_at_5
value: 7.639
- type: recall_at_1
value: 17.145
- type: recall_at_10
value: 33.938
- type: recall_at_100
value: 53.672
- type: recall_at_1000
value: 76.023
- type: recall_at_3
value: 25.363000000000003
- type: recall_at_5
value: 29.023
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.275
- type: map_at_10
value: 30.438
- type: map_at_100
value: 31.489
- type: map_at_1000
value: 31.601000000000003
- type: map_at_3
value: 28.647
- type: map_at_5
value: 29.660999999999998
- type: mrr_at_1
value: 28.077999999999996
- type: mrr_at_10
value: 34.098
- type: mrr_at_100
value: 35.025
- type: mrr_at_1000
value: 35.109
- type: mrr_at_3
value: 32.4
- type: mrr_at_5
value: 33.379999999999995
- type: ndcg_at_1
value: 28.077999999999996
- type: ndcg_at_10
value: 34.271
- type: ndcg_at_100
value: 39.352
- type: ndcg_at_1000
value: 42.199
- type: ndcg_at_3
value: 30.978
- type: ndcg_at_5
value: 32.498
- type: precision_at_1
value: 28.077999999999996
- type: precision_at_10
value: 5.345
- type: precision_at_100
value: 0.897
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 13.526
- type: precision_at_5
value: 9.16
- type: recall_at_1
value: 24.275
- type: recall_at_10
value: 42.362
- type: recall_at_100
value: 64.461
- type: recall_at_1000
value: 84.981
- type: recall_at_3
value: 33.249
- type: recall_at_5
value: 37.214999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.358
- type: map_at_10
value: 30.062
- type: map_at_100
value: 31.189
- type: map_at_1000
value: 31.386999999999997
- type: map_at_3
value: 27.672
- type: map_at_5
value: 28.76
- type: mrr_at_1
value: 26.877000000000002
- type: mrr_at_10
value: 33.948
- type: mrr_at_100
value: 34.746
- type: mrr_at_1000
value: 34.816
- type: mrr_at_3
value: 31.884
- type: mrr_at_5
value: 33.001000000000005
- type: ndcg_at_1
value: 26.877000000000002
- type: ndcg_at_10
value: 34.977000000000004
- type: ndcg_at_100
value: 39.753
- type: ndcg_at_1000
value: 42.866
- type: ndcg_at_3
value: 30.956
- type: ndcg_at_5
value: 32.381
- type: precision_at_1
value: 26.877000000000002
- type: precision_at_10
value: 6.7
- type: precision_at_100
value: 1.287
- type: precision_at_1000
value: 0.215
- type: precision_at_3
value: 14.360999999999999
- type: precision_at_5
value: 10.119
- type: recall_at_1
value: 22.358
- type: recall_at_10
value: 44.183
- type: recall_at_100
value: 67.14
- type: recall_at_1000
value: 87.53999999999999
- type: recall_at_3
value: 32.79
- type: recall_at_5
value: 36.829
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.198999999999998
- type: map_at_10
value: 25.229000000000003
- type: map_at_100
value: 26.003
- type: map_at_1000
value: 26.111
- type: map_at_3
value: 23.442
- type: map_at_5
value: 24.343
- type: mrr_at_1
value: 21.072
- type: mrr_at_10
value: 27.02
- type: mrr_at_100
value: 27.735
- type: mrr_at_1000
value: 27.815
- type: mrr_at_3
value: 25.416
- type: mrr_at_5
value: 26.173999999999996
- type: ndcg_at_1
value: 21.072
- type: ndcg_at_10
value: 28.862
- type: ndcg_at_100
value: 33.043
- type: ndcg_at_1000
value: 36.003
- type: ndcg_at_3
value: 25.35
- type: ndcg_at_5
value: 26.773000000000003
- type: precision_at_1
value: 21.072
- type: precision_at_10
value: 4.436
- type: precision_at_100
value: 0.713
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 10.659
- type: precision_at_5
value: 7.32
- type: recall_at_1
value: 19.198999999999998
- type: recall_at_10
value: 38.376
- type: recall_at_100
value: 58.36900000000001
- type: recall_at_1000
value: 80.92099999999999
- type: recall_at_3
value: 28.715000000000003
- type: recall_at_5
value: 32.147
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.9319999999999995
- type: map_at_10
value: 10.483
- type: map_at_100
value: 11.97
- type: map_at_1000
value: 12.171999999999999
- type: map_at_3
value: 8.477
- type: map_at_5
value: 9.495000000000001
- type: mrr_at_1
value: 13.094
- type: mrr_at_10
value: 21.282
- type: mrr_at_100
value: 22.556
- type: mrr_at_1000
value: 22.628999999999998
- type: mrr_at_3
value: 18.218999999999998
- type: mrr_at_5
value: 19.900000000000002
- type: ndcg_at_1
value: 13.094
- type: ndcg_at_10
value: 15.811
- type: ndcg_at_100
value: 23.035
- type: ndcg_at_1000
value: 27.089999999999996
- type: ndcg_at_3
value: 11.905000000000001
- type: ndcg_at_5
value: 13.377
- type: precision_at_1
value: 13.094
- type: precision_at_10
value: 5.225
- type: precision_at_100
value: 1.2970000000000002
- type: precision_at_1000
value: 0.203
- type: precision_at_3
value: 8.86
- type: precision_at_5
value: 7.309
- type: recall_at_1
value: 5.9319999999999995
- type: recall_at_10
value: 20.305
- type: recall_at_100
value: 46.314
- type: recall_at_1000
value: 69.612
- type: recall_at_3
value: 11.21
- type: recall_at_5
value: 14.773
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.674
- type: map_at_10
value: 17.822
- type: map_at_100
value: 24.794
- type: map_at_1000
value: 26.214
- type: map_at_3
value: 12.690999999999999
- type: map_at_5
value: 15.033
- type: mrr_at_1
value: 61.75000000000001
- type: mrr_at_10
value: 71.58
- type: mrr_at_100
value: 71.923
- type: mrr_at_1000
value: 71.932
- type: mrr_at_3
value: 70.125
- type: mrr_at_5
value: 71.038
- type: ndcg_at_1
value: 51
- type: ndcg_at_10
value: 38.637
- type: ndcg_at_100
value: 42.398
- type: ndcg_at_1000
value: 48.962
- type: ndcg_at_3
value: 43.29
- type: ndcg_at_5
value: 40.763
- type: precision_at_1
value: 61.75000000000001
- type: precision_at_10
value: 30.125
- type: precision_at_100
value: 9.53
- type: precision_at_1000
value: 1.9619999999999997
- type: precision_at_3
value: 45.583
- type: precision_at_5
value: 38.95
- type: recall_at_1
value: 8.674
- type: recall_at_10
value: 23.122
- type: recall_at_100
value: 47.46
- type: recall_at_1000
value: 67.662
- type: recall_at_3
value: 13.946
- type: recall_at_5
value: 17.768
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.86000000000001
- type: f1
value: 41.343580452760776
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.609
- type: map_at_10
value: 47.552
- type: map_at_100
value: 48.283
- type: map_at_1000
value: 48.321
- type: map_at_3
value: 44.869
- type: map_at_5
value: 46.509
- type: mrr_at_1
value: 39.214
- type: mrr_at_10
value: 50.434999999999995
- type: mrr_at_100
value: 51.122
- type: mrr_at_1000
value: 51.151
- type: mrr_at_3
value: 47.735
- type: mrr_at_5
value: 49.394
- type: ndcg_at_1
value: 39.214
- type: ndcg_at_10
value: 53.52400000000001
- type: ndcg_at_100
value: 56.997
- type: ndcg_at_1000
value: 57.975
- type: ndcg_at_3
value: 48.173
- type: ndcg_at_5
value: 51.05800000000001
- type: precision_at_1
value: 39.214
- type: precision_at_10
value: 7.573
- type: precision_at_100
value: 0.9440000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 19.782
- type: precision_at_5
value: 13.453000000000001
- type: recall_at_1
value: 36.609
- type: recall_at_10
value: 69.247
- type: recall_at_100
value: 84.99600000000001
- type: recall_at_1000
value: 92.40899999999999
- type: recall_at_3
value: 54.856
- type: recall_at_5
value: 61.797000000000004
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.466
- type: map_at_10
value: 27.060000000000002
- type: map_at_100
value: 28.511999999999997
- type: map_at_1000
value: 28.693
- type: map_at_3
value: 22.777
- type: map_at_5
value: 25.086000000000002
- type: mrr_at_1
value: 32.716
- type: mrr_at_10
value: 41.593999999999994
- type: mrr_at_100
value: 42.370000000000005
- type: mrr_at_1000
value: 42.419000000000004
- type: mrr_at_3
value: 38.143
- type: mrr_at_5
value: 40.288000000000004
- type: ndcg_at_1
value: 32.716
- type: ndcg_at_10
value: 34.795
- type: ndcg_at_100
value: 40.58
- type: ndcg_at_1000
value: 43.993
- type: ndcg_at_3
value: 29.573
- type: ndcg_at_5
value: 31.583
- type: precision_at_1
value: 32.716
- type: precision_at_10
value: 9.937999999999999
- type: precision_at_100
value: 1.585
- type: precision_at_1000
value: 0.22
- type: precision_at_3
value: 19.496
- type: precision_at_5
value: 15.247
- type: recall_at_1
value: 16.466
- type: recall_at_10
value: 42.886
- type: recall_at_100
value: 64.724
- type: recall_at_1000
value: 85.347
- type: recall_at_3
value: 26.765
- type: recall_at_5
value: 33.603
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.025
- type: map_at_10
value: 47.343
- type: map_at_100
value: 48.207
- type: map_at_1000
value: 48.281
- type: map_at_3
value: 44.519
- type: map_at_5
value: 46.217000000000006
- type: mrr_at_1
value: 66.05
- type: mrr_at_10
value: 72.94699999999999
- type: mrr_at_100
value: 73.289
- type: mrr_at_1000
value: 73.30499999999999
- type: mrr_at_3
value: 71.686
- type: mrr_at_5
value: 72.491
- type: ndcg_at_1
value: 66.05
- type: ndcg_at_10
value: 56.338
- type: ndcg_at_100
value: 59.599999999999994
- type: ndcg_at_1000
value: 61.138000000000005
- type: ndcg_at_3
value: 52.034000000000006
- type: ndcg_at_5
value: 54.352000000000004
- type: precision_at_1
value: 66.05
- type: precision_at_10
value: 11.693000000000001
- type: precision_at_100
value: 1.425
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 32.613
- type: precision_at_5
value: 21.401999999999997
- type: recall_at_1
value: 33.025
- type: recall_at_10
value: 58.467
- type: recall_at_100
value: 71.242
- type: recall_at_1000
value: 81.452
- type: recall_at_3
value: 48.92
- type: recall_at_5
value: 53.504
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 75.5492
- type: ap
value: 69.42911637216271
- type: f1
value: 75.39113704261024
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.173
- type: map_at_10
value: 35.453
- type: map_at_100
value: 36.573
- type: map_at_1000
value: 36.620999999999995
- type: map_at_3
value: 31.655
- type: map_at_5
value: 33.823
- type: mrr_at_1
value: 23.868000000000002
- type: mrr_at_10
value: 36.085
- type: mrr_at_100
value: 37.15
- type: mrr_at_1000
value: 37.193
- type: mrr_at_3
value: 32.376
- type: mrr_at_5
value: 34.501
- type: ndcg_at_1
value: 23.854
- type: ndcg_at_10
value: 42.33
- type: ndcg_at_100
value: 47.705999999999996
- type: ndcg_at_1000
value: 48.91
- type: ndcg_at_3
value: 34.604
- type: ndcg_at_5
value: 38.473
- type: precision_at_1
value: 23.854
- type: precision_at_10
value: 6.639
- type: precision_at_100
value: 0.932
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.685
- type: precision_at_5
value: 10.782
- type: recall_at_1
value: 23.173
- type: recall_at_10
value: 63.441
- type: recall_at_100
value: 88.25
- type: recall_at_1000
value: 97.438
- type: recall_at_3
value: 42.434
- type: recall_at_5
value: 51.745
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.05426356589147
- type: f1
value: 91.88068588063942
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 73.23985408116735
- type: f1
value: 55.858906745287506
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.21923335574984
- type: f1
value: 70.0174116204253
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.77673167451245
- type: f1
value: 75.44811354778666
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.340414710728737
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.196676760061578
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 29.564149683482206
- type: mrr
value: 30.28995474250486
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.93
- type: map_at_10
value: 12.828000000000001
- type: map_at_100
value: 15.501000000000001
- type: map_at_1000
value: 16.791
- type: map_at_3
value: 9.727
- type: map_at_5
value: 11.318999999999999
- type: mrr_at_1
value: 47.678
- type: mrr_at_10
value: 55.893
- type: mrr_at_100
value: 56.491
- type: mrr_at_1000
value: 56.53
- type: mrr_at_3
value: 54.386
- type: mrr_at_5
value: 55.516
- type: ndcg_at_1
value: 45.975
- type: ndcg_at_10
value: 33.928999999999995
- type: ndcg_at_100
value: 30.164
- type: ndcg_at_1000
value: 38.756
- type: ndcg_at_3
value: 41.077000000000005
- type: ndcg_at_5
value: 38.415
- type: precision_at_1
value: 47.678
- type: precision_at_10
value: 24.365000000000002
- type: precision_at_100
value: 7.344
- type: precision_at_1000
value: 1.994
- type: precision_at_3
value: 38.184000000000005
- type: precision_at_5
value: 33.003
- type: recall_at_1
value: 5.93
- type: recall_at_10
value: 16.239
- type: recall_at_100
value: 28.782999999999998
- type: recall_at_1000
value: 60.11
- type: recall_at_3
value: 10.700999999999999
- type: recall_at_5
value: 13.584
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.163000000000004
- type: map_at_10
value: 51.520999999999994
- type: map_at_100
value: 52.449
- type: map_at_1000
value: 52.473000000000006
- type: map_at_3
value: 47.666
- type: map_at_5
value: 50.043000000000006
- type: mrr_at_1
value: 40.266999999999996
- type: mrr_at_10
value: 54.074
- type: mrr_at_100
value: 54.722
- type: mrr_at_1000
value: 54.739000000000004
- type: mrr_at_3
value: 51.043000000000006
- type: mrr_at_5
value: 52.956
- type: ndcg_at_1
value: 40.238
- type: ndcg_at_10
value: 58.73199999999999
- type: ndcg_at_100
value: 62.470000000000006
- type: ndcg_at_1000
value: 63.083999999999996
- type: ndcg_at_3
value: 51.672
- type: ndcg_at_5
value: 55.564
- type: precision_at_1
value: 40.238
- type: precision_at_10
value: 9.279
- type: precision_at_100
value: 1.139
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 23.078000000000003
- type: precision_at_5
value: 16.176
- type: recall_at_1
value: 36.163000000000004
- type: recall_at_10
value: 77.88199999999999
- type: recall_at_100
value: 93.83399999999999
- type: recall_at_1000
value: 98.465
- type: recall_at_3
value: 59.857000000000006
- type: recall_at_5
value: 68.73599999999999
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.344
- type: map_at_10
value: 83.907
- type: map_at_100
value: 84.536
- type: map_at_1000
value: 84.557
- type: map_at_3
value: 80.984
- type: map_at_5
value: 82.844
- type: mrr_at_1
value: 81.02000000000001
- type: mrr_at_10
value: 87.158
- type: mrr_at_100
value: 87.268
- type: mrr_at_1000
value: 87.26899999999999
- type: mrr_at_3
value: 86.17
- type: mrr_at_5
value: 86.87
- type: ndcg_at_1
value: 81.02000000000001
- type: ndcg_at_10
value: 87.70700000000001
- type: ndcg_at_100
value: 89.004
- type: ndcg_at_1000
value: 89.139
- type: ndcg_at_3
value: 84.841
- type: ndcg_at_5
value: 86.455
- type: precision_at_1
value: 81.02000000000001
- type: precision_at_10
value: 13.248999999999999
- type: precision_at_100
value: 1.516
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.963
- type: precision_at_5
value: 24.33
- type: recall_at_1
value: 70.344
- type: recall_at_10
value: 94.75099999999999
- type: recall_at_100
value: 99.30499999999999
- type: recall_at_1000
value: 99.928
- type: recall_at_3
value: 86.506
- type: recall_at_5
value: 91.083
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 42.873718018378305
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 56.39477366450528
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.868
- type: map_at_10
value: 9.611
- type: map_at_100
value: 11.087
- type: map_at_1000
value: 11.332
- type: map_at_3
value: 6.813
- type: map_at_5
value: 8.233
- type: mrr_at_1
value: 19
- type: mrr_at_10
value: 28.457
- type: mrr_at_100
value: 29.613
- type: mrr_at_1000
value: 29.695
- type: mrr_at_3
value: 25.55
- type: mrr_at_5
value: 27.29
- type: ndcg_at_1
value: 19
- type: ndcg_at_10
value: 16.419
- type: ndcg_at_100
value: 22.817999999999998
- type: ndcg_at_1000
value: 27.72
- type: ndcg_at_3
value: 15.379000000000001
- type: ndcg_at_5
value: 13.645
- type: precision_at_1
value: 19
- type: precision_at_10
value: 8.540000000000001
- type: precision_at_100
value: 1.7819999999999998
- type: precision_at_1000
value: 0.297
- type: precision_at_3
value: 14.267
- type: precision_at_5
value: 12.04
- type: recall_at_1
value: 3.868
- type: recall_at_10
value: 17.288
- type: recall_at_100
value: 36.144999999999996
- type: recall_at_1000
value: 60.199999999999996
- type: recall_at_3
value: 8.688
- type: recall_at_5
value: 12.198
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.96614722598582
- type: cos_sim_spearman
value: 78.9003023008781
- type: euclidean_pearson
value: 81.01829384436505
- type: euclidean_spearman
value: 78.93248416788914
- type: manhattan_pearson
value: 81.1665428926402
- type: manhattan_spearman
value: 78.93264116287453
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.54613363895993
- type: cos_sim_spearman
value: 75.1883451602451
- type: euclidean_pearson
value: 79.70320886899894
- type: euclidean_spearman
value: 74.5917140136796
- type: manhattan_pearson
value: 79.82157067185999
- type: manhattan_spearman
value: 74.74185720594735
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 81.30430156721782
- type: cos_sim_spearman
value: 81.79962989974364
- type: euclidean_pearson
value: 80.89058823224924
- type: euclidean_spearman
value: 81.35929372984597
- type: manhattan_pearson
value: 81.12204370487478
- type: manhattan_spearman
value: 81.6248963282232
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 81.13064504403134
- type: cos_sim_spearman
value: 78.48371403924872
- type: euclidean_pearson
value: 80.16794919665591
- type: euclidean_spearman
value: 78.29216082221699
- type: manhattan_pearson
value: 80.22308565207301
- type: manhattan_spearman
value: 78.37829229948022
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.52918899541099
- type: cos_sim_spearman
value: 87.49276894673142
- type: euclidean_pearson
value: 86.77440570164254
- type: euclidean_spearman
value: 87.5753295736756
- type: manhattan_pearson
value: 86.86098573892133
- type: manhattan_spearman
value: 87.65848591821947
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.86805307244882
- type: cos_sim_spearman
value: 84.58066253757511
- type: euclidean_pearson
value: 84.38377000876991
- type: euclidean_spearman
value: 85.1837278784528
- type: manhattan_pearson
value: 84.41903291363842
- type: manhattan_spearman
value: 85.19023736251052
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.77218560282436
- type: cos_sim_spearman
value: 87.94243515296604
- type: euclidean_pearson
value: 88.22800939214864
- type: euclidean_spearman
value: 87.91106839439841
- type: manhattan_pearson
value: 88.17063269848741
- type: manhattan_spearman
value: 87.72751904126062
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 60.40731554300387
- type: cos_sim_spearman
value: 63.76300532966479
- type: euclidean_pearson
value: 62.94727878229085
- type: euclidean_spearman
value: 63.678039531461216
- type: manhattan_pearson
value: 63.00661039863549
- type: manhattan_spearman
value: 63.6282591984376
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.92731569745344
- type: cos_sim_spearman
value: 86.36336704300167
- type: euclidean_pearson
value: 86.09122224841195
- type: euclidean_spearman
value: 86.2116149319238
- type: manhattan_pearson
value: 86.07879456717032
- type: manhattan_spearman
value: 86.2022069635119
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.75976311752326
- type: mrr
value: 94.15782837351466
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 51.193999999999996
- type: map_at_10
value: 61.224999999999994
- type: map_at_100
value: 62.031000000000006
- type: map_at_1000
value: 62.066
- type: map_at_3
value: 59.269000000000005
- type: map_at_5
value: 60.159
- type: mrr_at_1
value: 53.667
- type: mrr_at_10
value: 62.74999999999999
- type: mrr_at_100
value: 63.39399999999999
- type: mrr_at_1000
value: 63.425
- type: mrr_at_3
value: 61.389
- type: mrr_at_5
value: 61.989000000000004
- type: ndcg_at_1
value: 53.667
- type: ndcg_at_10
value: 65.596
- type: ndcg_at_100
value: 68.906
- type: ndcg_at_1000
value: 69.78999999999999
- type: ndcg_at_3
value: 62.261
- type: ndcg_at_5
value: 63.453
- type: precision_at_1
value: 53.667
- type: precision_at_10
value: 8.667
- type: precision_at_100
value: 1.04
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 24.556
- type: precision_at_5
value: 15.6
- type: recall_at_1
value: 51.193999999999996
- type: recall_at_10
value: 77.156
- type: recall_at_100
value: 91.43299999999999
- type: recall_at_1000
value: 98.333
- type: recall_at_3
value: 67.994
- type: recall_at_5
value: 71.14399999999999
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.81485148514851
- type: cos_sim_ap
value: 95.28896513388551
- type: cos_sim_f1
value: 90.43478260869566
- type: cos_sim_precision
value: 92.56544502617801
- type: cos_sim_recall
value: 88.4
- type: dot_accuracy
value: 99.30594059405941
- type: dot_ap
value: 61.6432597455472
- type: dot_f1
value: 59.46481665014866
- type: dot_precision
value: 58.93909626719057
- type: dot_recall
value: 60
- type: euclidean_accuracy
value: 99.81980198019802
- type: euclidean_ap
value: 95.21411049527
- type: euclidean_f1
value: 91.06090373280944
- type: euclidean_precision
value: 89.47876447876449
- type: euclidean_recall
value: 92.7
- type: manhattan_accuracy
value: 99.81782178217821
- type: manhattan_ap
value: 95.32449994414968
- type: manhattan_f1
value: 90.86395233366436
- type: manhattan_precision
value: 90.23668639053254
- type: manhattan_recall
value: 91.5
- type: max_accuracy
value: 99.81980198019802
- type: max_ap
value: 95.32449994414968
- type: max_f1
value: 91.06090373280944
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 59.08045614613064
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 30.297802606804748
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.12801740706292
- type: mrr
value: 50.05592956879722
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.523347880124497
- type: cos_sim_spearman
value: 31.388214436391014
- type: dot_pearson
value: 24.55403435439901
- type: dot_spearman
value: 23.50153210841191
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.243
- type: map_at_10
value: 1.886
- type: map_at_100
value: 10.040000000000001
- type: map_at_1000
value: 23.768
- type: map_at_3
value: 0.674
- type: map_at_5
value: 1.079
- type: mrr_at_1
value: 88
- type: mrr_at_10
value: 93.667
- type: mrr_at_100
value: 93.667
- type: mrr_at_1000
value: 93.667
- type: mrr_at_3
value: 93.667
- type: mrr_at_5
value: 93.667
- type: ndcg_at_1
value: 83
- type: ndcg_at_10
value: 76.777
- type: ndcg_at_100
value: 55.153
- type: ndcg_at_1000
value: 47.912
- type: ndcg_at_3
value: 81.358
- type: ndcg_at_5
value: 80.74799999999999
- type: precision_at_1
value: 88
- type: precision_at_10
value: 80.80000000000001
- type: precision_at_100
value: 56.02
- type: precision_at_1000
value: 21.51
- type: precision_at_3
value: 86
- type: precision_at_5
value: 86
- type: recall_at_1
value: 0.243
- type: recall_at_10
value: 2.0869999999999997
- type: recall_at_100
value: 13.014000000000001
- type: recall_at_1000
value: 44.433
- type: recall_at_3
value: 0.6910000000000001
- type: recall_at_5
value: 1.1440000000000001
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.066
- type: map_at_10
value: 10.615
- type: map_at_100
value: 16.463
- type: map_at_1000
value: 17.815
- type: map_at_3
value: 5.7860000000000005
- type: map_at_5
value: 7.353999999999999
- type: mrr_at_1
value: 38.775999999999996
- type: mrr_at_10
value: 53.846000000000004
- type: mrr_at_100
value: 54.37
- type: mrr_at_1000
value: 54.37
- type: mrr_at_3
value: 48.980000000000004
- type: mrr_at_5
value: 51.735
- type: ndcg_at_1
value: 34.694
- type: ndcg_at_10
value: 26.811
- type: ndcg_at_100
value: 37.342999999999996
- type: ndcg_at_1000
value: 47.964
- type: ndcg_at_3
value: 30.906
- type: ndcg_at_5
value: 27.77
- type: precision_at_1
value: 38.775999999999996
- type: precision_at_10
value: 23.878
- type: precision_at_100
value: 7.632999999999999
- type: precision_at_1000
value: 1.469
- type: precision_at_3
value: 31.973000000000003
- type: precision_at_5
value: 26.939
- type: recall_at_1
value: 3.066
- type: recall_at_10
value: 17.112
- type: recall_at_100
value: 47.723
- type: recall_at_1000
value: 79.50500000000001
- type: recall_at_3
value: 6.825
- type: recall_at_5
value: 9.584
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 72.76460000000002
- type: ap
value: 14.944240012137053
- type: f1
value: 55.89805777266571
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 63.30503678551217
- type: f1
value: 63.57492701921179
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 37.51066495006874
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.07021517553794
- type: cos_sim_ap
value: 74.15520712370555
- type: cos_sim_f1
value: 68.64321608040201
- type: cos_sim_precision
value: 65.51558752997602
- type: cos_sim_recall
value: 72.0844327176781
- type: dot_accuracy
value: 80.23484532395541
- type: dot_ap
value: 54.298763810214176
- type: dot_f1
value: 53.22254659779924
- type: dot_precision
value: 46.32525410476936
- type: dot_recall
value: 62.532981530343015
- type: euclidean_accuracy
value: 86.04637301066937
- type: euclidean_ap
value: 73.85333854233123
- type: euclidean_f1
value: 68.77723660599845
- type: euclidean_precision
value: 66.87437686939182
- type: euclidean_recall
value: 70.79155672823218
- type: manhattan_accuracy
value: 85.98676759849795
- type: manhattan_ap
value: 73.56016090035973
- type: manhattan_f1
value: 68.48878539036647
- type: manhattan_precision
value: 63.9505607690547
- type: manhattan_recall
value: 73.7203166226913
- type: max_accuracy
value: 86.07021517553794
- type: max_ap
value: 74.15520712370555
- type: max_f1
value: 68.77723660599845
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.92769821865176
- type: cos_sim_ap
value: 85.78879502899773
- type: cos_sim_f1
value: 78.14414083990464
- type: cos_sim_precision
value: 74.61651607480563
- type: cos_sim_recall
value: 82.0218663381583
- type: dot_accuracy
value: 84.95750378390964
- type: dot_ap
value: 75.80219641857563
- type: dot_f1
value: 70.13966179585681
- type: dot_precision
value: 65.71140262361251
- type: dot_recall
value: 75.20788420080073
- type: euclidean_accuracy
value: 88.93546008460433
- type: euclidean_ap
value: 85.72056428301667
- type: euclidean_f1
value: 78.14387902598124
- type: euclidean_precision
value: 75.3376688344172
- type: euclidean_recall
value: 81.16723129042192
- type: manhattan_accuracy
value: 88.96262661543835
- type: manhattan_ap
value: 85.76605136314335
- type: manhattan_f1
value: 78.26696165191743
- type: manhattan_precision
value: 75.0990659496179
- type: manhattan_recall
value: 81.71388974437943
- type: max_accuracy
value: 88.96262661543835
- type: max_ap
value: 85.78879502899773
- type: max_f1
value: 78.26696165191743
language:
- en
license: mit
---
# E5-small
**News (May 2023): please switch to [e5-small-v2](https://huggingface.co/intfloat/e5-small-v2), which has better performance and same method of usage.**
[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
This model has 12 layers and the embedding size is 384.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."]
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-small')
model = AutoModel.from_pretrained('intfloat/e5-small')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Training Details
Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
## Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/e5-small')
input_texts = [
'query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2022text,
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2212.03533},
year={2022}
}
```
## Limitations
This model only works for English texts. Long texts will be truncated to at most 512 tokens.
|
KnutJaegersberg/galactica-orca-wizardlm-1.3b
|
KnutJaegersberg
| 2023-08-07T04:50:46Z | 1,439 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"opt",
"text-generation",
"autotrain",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-26T13:09:09Z |
---
tags:
- autotrain
- text-generation
license: cc-by-nc-4.0
---
Prompt
### System:\n{system}\n\n### User:\n{instruction}\n\n### Response:\n
|
thisiskeithkwan/whisper-medium-1000steps-spaced
|
thisiskeithkwan
| 2023-08-07T04:35:59Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"zh",
"dataset:thisiskeithkwan/canto",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-07T01:25:37Z |
---
language:
- zh
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- thisiskeithkwan/canto
model-index:
- name: whisper-medium-cantonese
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-cantonese
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the thisiskeithkwan/canto dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4767
- Cer: 1.2115
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5362 | 0.76 | 500 | 0.4981 | 1.5560 |
| 0.3313 | 1.52 | 1000 | 0.4767 | 1.2115 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
elenahuang/llama2-qlora-finetunined-french
|
elenahuang
| 2023-08-07T03:55:05Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-07T03:54:59Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
avanish07/Hindi-wiki-LLaMA
|
avanish07
| 2023-08-07T03:53:11Z | 6 | 0 |
peft
|
[
"peft",
"text-generation",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-08-06T15:27:35Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: peft
---
## Hindi-wiki-LLaMA
Hindi Wikipedia Article Generation Model
This repository contains a language generation model trained on Hindi Wikipedia articles using the Hugging Face Transformers library. The model is based on the Llama-2 architecture and fine-tuned on a large dataset of Hindi text from Wikipedia.
## Model Details
- Base Model: Llama-2
- Pretraining Dataset: Hindi Wikipedia Articles
- Tokenizer: Hugging Face Tokenizer
- Model Architecture: Causal Language Modeling
```python
from peft import AutoPeftModelForCausalLM
base_model_name = "meta-llama/Llama-2-7b-hf"
tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
output_dir = "./final_checkpoint"
device_map = {"": 0}
model = AutoPeftModelForCausalLM.from_pretrained(output_dir, device_map=device_map, torch_dtype=torch.bfloat16)
device = torch.device("cuda")
text = ""
inputs = tokenizer(text, return_tensors="pt").to(device)
outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), attention_mask=inputs["attention_mask"], max_new_tokens=100, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs["input_ids"][0]):], skip_special_tokens=True))
```
## Model Performance:--
The model has been trained on a substantial amount of Hindi Wikipedia articles, which allows it to generate coherent and contextually relevant text.
|
hw2942/chinese-bigbird-wwm-base-4096-wallstreetcn-morning-news-market-overview-open-SSEC-f1-v1
|
hw2942
| 2023-08-07T03:50:49Z | 97 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"big_bird",
"text-classification",
"generated_from_trainer",
"base_model:Lowin/chinese-bigbird-wwm-base-4096",
"base_model:finetune:Lowin/chinese-bigbird-wwm-base-4096",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-07T03:23:10Z |
---
license: apache-2.0
base_model: Lowin/chinese-bigbird-wwm-base-4096
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-bigbird-wwm-base-4096-wallstreetcn-morning-news-market-overview-open-SSEC-f1-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-bigbird-wwm-base-4096-wallstreetcn-morning-news-market-overview-open-SSEC-f1-v1
This model is a fine-tuned version of [Lowin/chinese-bigbird-wwm-base-4096](https://huggingface.co/Lowin/chinese-bigbird-wwm-base-4096) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9660
- F1: 0.5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 75 | 0.6832 | 0.1538 |
| No log | 2.0 | 150 | 0.6909 | 0.0 |
| No log | 3.0 | 225 | 0.6766 | 0.4 |
| No log | 4.0 | 300 | 0.9574 | 0.5161 |
| No log | 5.0 | 375 | 1.0109 | 0.4348 |
| No log | 6.0 | 450 | 1.1757 | 0.3333 |
| 0.5475 | 7.0 | 525 | 1.6141 | 0.5 |
| 0.5475 | 8.0 | 600 | 1.7908 | 0.3810 |
| 0.5475 | 9.0 | 675 | 1.9172 | 0.5 |
| 0.5475 | 10.0 | 750 | 1.9660 | 0.5 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
thisiskeithkwan/whisper-medium-1000steps
|
thisiskeithkwan
| 2023-08-07T03:50:36Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"zh",
"dataset:thisiskeithkwan/canto",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-07T01:06:39Z |
---
language:
- zh
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- thisiskeithkwan/canto
model-index:
- name: whisper-medium-cantonese
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-cantonese
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the thisiskeithkwan/canto dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7006
- Cer: 3.6111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6458 | 0.76 | 500 | 0.7109 | 3.5960 |
| 0.4183 | 1.52 | 1000 | 0.7006 | 3.6111 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
mikuhl/wow-icons
|
mikuhl
| 2023-08-07T03:46:47Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-08-07T03:12:35Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a world of warcraft icon
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - mikuhl/wow-icons
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a world of warcraft icon using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
yourui/bgi-promptcblue-baichuan-13b
|
yourui
| 2023-08-07T03:31:49Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"baichuan",
"text-generation",
"generated_from_trainer",
"custom_code",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-27T02:45:19Z |
---
tags:
- generated_from_trainer
datasets:
- Data/PromptCBLUE/bgiv3
model-index:
- name: baichuan_finetune_v3_071618
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# baichuan_finetune_v3_071618
This model is a fine-tuned version of [baichuan-inc/Baichuan-13B-Base](https://huggingface.co/baichuan-inc/Baichuan-13B-Base) on the Data/PromptCBLUE/bgiv3 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Naruke/rl_course_vizdoom_health_gathering_supreme
|
Naruke
| 2023-08-07T03:22:50Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T19:03:46Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.87 +/- 5.84
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Naruke/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
GFazzito/distilhubert-finetuned-gtzan
|
GFazzito
| 2023-08-07T03:21:29Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-21T00:04:55Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.82
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5513
- Accuracy: 0.82
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9949 | 1.0 | 113 | 1.8096 | 0.5 |
| 1.3453 | 2.0 | 226 | 1.2502 | 0.62 |
| 1.0267 | 3.0 | 339 | 0.9683 | 0.73 |
| 0.8382 | 4.0 | 452 | 0.8201 | 0.74 |
| 0.6864 | 5.0 | 565 | 0.6620 | 0.81 |
| 0.3746 | 6.0 | 678 | 0.8011 | 0.74 |
| 0.2883 | 7.0 | 791 | 0.5384 | 0.86 |
| 0.1192 | 8.0 | 904 | 0.4698 | 0.85 |
| 0.2028 | 9.0 | 1017 | 0.4610 | 0.85 |
| 0.1638 | 10.0 | 1130 | 0.5513 | 0.82 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Eggsbena/model_009
|
Eggsbena
| 2023-08-07T03:17:45Z | 29 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-07T03:04:57Z |
---
library_name: diffusers
pipeline_tag: text-to-image
---
|
Eggsbena/model_008
|
Eggsbena
| 2023-08-07T03:09:38Z | 29 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-07T02:57:23Z |
---
library_name: diffusers
pipeline_tag: text-to-image
---
|
saefro991/tts_bytes_css10_7lang_textpretrain_residual_freeze
|
saefro991
| 2023-08-07T03:01:26Z | 3 | 1 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"multilingual",
"dataset:masmultts",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2023-08-07T02:45:09Z |
---
tags:
- espnet
- audio
- text-to-speech
language: multilingual
datasets:
- masmultts
license: cc-by-4.0
---
## ESPnet2 TTS model
### `saefro991/tts_bytes_css10_7lang_textpretrain_residual_freeze`
This model was trained by Takaaki-Saeki using masmultts recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 11a7d61312439111d4996d55935ede718d494262
pip install -e .
cd egs2/masmultts/tts_byte_css10_adap_residual_freeze
./run.sh --skip_data_prep false --skip_train true --download_model saefro991/tts_bytes_css10_7lang_textpretrain_residual_freeze
```
## TTS config
<details><summary>expand</summary>
```
config: conf/train.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/tts_train_raw_byte
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 1
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 200
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
- - train
- loss
- min
keep_nbest_models: 3
nbest_averaging_interval: 0
grad_clip: 2.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param:
- ../tts_pretrain_byte_residual/exp/tts_train_byte/2epoch.pth:tts_pretrain.encoder:tts.encoder
- ../tts_pretrain_byte_residual/exp/tts_train_byte/2epoch.pth:tts_pretrain.lid_emb:tts.lid_emb
ignore_init_mismatch: false
freeze_param:
- tts.encoder.adapter
- tts.encoder.embed
- tts.lid_emb
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 400000
valid_batch_bins: null
train_shape_file:
- exp/tts_stats_raw_byte/train/text_shape.byte
- exp/tts_stats_raw_byte/train/speech_shape
valid_shape_file:
- exp/tts_stats_raw_byte/valid/text_shape.byte
- exp/tts_stats_raw_byte/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - /local/11399690.1.gpu/dump/raw/train/text
- text
- text
- - /local/11399690.1.gpu/dump/raw/train/wav.scp
- speech
- sound
- - /local/11399690.1.gpu/dump/xvector/train/xvector.scp
- spembs
- kaldi_ark
- - /local/11399690.1.gpu/dump/raw/train/utt2lid
- lids
- text_int
valid_data_path_and_name_and_type:
- - /local/11399690.1.gpu/dump/raw/dev/text
- text
- text
- - /local/11399690.1.gpu/dump/raw/dev/wav.scp
- speech
- sound
- - /local/11399690.1.gpu/dump/xvector/dev/xvector.scp
- spembs
- kaldi_ark
- - /local/11399690.1.gpu/dump/raw/dev/utt2lid
- lids
- text_int
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 1.0
scheduler: noamlr
scheduler_conf:
model_size: 512
warmup_steps: 50000
token_list:
- <blank>
- <unk>
- '32'
- '101'
- '97'
- '105'
- '110'
- '116'
- '111'
- '115'
- '114'
- '108'
- '100'
- '117'
- '109'
- '99'
- '195'
- '112'
- '104'
- '118'
- '107'
- '103'
- '98'
- '122'
- '102'
- '106'
- '121'
- '119'
- '164'
- '169'
- '197'
- '196'
- '161'
- '113'
- '179'
- '173'
- '188'
- '182'
- '190'
- '208'
- '120'
- '141'
- '153'
- '160'
- '155'
- '189'
- '131'
- '186'
- '168'
- '133'
- '209'
- '130'
- '181'
- '159'
- '151'
- '175'
- '177'
- '145'
- '171'
- '174'
- '165'
- '135'
- '200'
- '180'
- '170'
- '178'
- '176'
- '163'
- '184'
- '185'
- '187'
- '129'
- '132'
- '128'
- '136'
- '143'
- '162'
- '191'
- '150'
- '206'
- '183'
- '140'
- '172'
- '167'
- '207'
- '139'
- '142'
- '147'
- '134'
- '137'
- '148'
- '194'
- '149'
- '166'
- '49'
- '50'
- '48'
- '51'
- '138'
- '56'
- '53'
- '55'
- '52'
- '54'
- '57'
- '199'
- '226'
- '210'
- '144'
- '203'
- '225'
- '202'
- '232'
- '201'
- '157'
- '231'
- '156'
- '220'
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: byte
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: byte
feats_extract: fbank
feats_extract_conf:
n_fft: 1024
hop_length: 256
win_length: null
fs: 16000
fmin: 80
fmax: 7600
n_mels: 80
normalize: global_mvn
normalize_conf:
stats_file: exp/tts_stats_raw_byte/train/feats_stats.npz
tts: transformer
tts_conf:
embed_dim: 0
eprenet_conv_layers: 0
eprenet_conv_filts: 0
eprenet_conv_chans: 0
dprenet_layers: 2
dprenet_units: 256
adim: 512
aheads: 8
elayers: 6
eunits: 1024
dlayers: 6
dunits: 1024
positionwise_layer_type: conv1d
positionwise_conv_kernel_size: 1
postnet_layers: 5
postnet_filts: 5
postnet_chans: 256
spk_embed_dim: 192
spk_embed_integration_type: add
use_gst: true
gst_heads: 4
gst_tokens: 16
use_masking: true
bce_pos_weight: 5.0
use_scaled_pos_enc: true
encoder_normalize_before: true
decoder_normalize_before: true
reduction_factor: 1
init_type: xavier_uniform
init_enc_alpha: 1.0
init_dec_alpha: 1.0
eprenet_dropout_rate: 0.0
dprenet_dropout_rate: 0.5
postnet_dropout_rate: 0.5
transformer_enc_dropout_rate: 0.1
transformer_enc_positional_dropout_rate: 0.1
transformer_enc_attn_dropout_rate: 0.1
transformer_dec_dropout_rate: 0.1
transformer_dec_positional_dropout_rate: 0.1
transformer_dec_attn_dropout_rate: 0.1
transformer_enc_dec_attn_dropout_rate: 0.1
use_guided_attn_loss: true
num_heads_applied_guided_attn: 2
num_layers_applied_guided_attn: 2
modules_applied_guided_attn:
- encoder-decoder
guided_attn_loss_sigma: 0.4
guided_attn_loss_lambda: 10.0
langs: 21
lang_family_encoding: false
num_lang_family: 7
use_adapter: true
adapter_type: residual
use_encoder_w_lid: true
pitch_extract: null
pitch_extract_conf: {}
pitch_normalize: null
pitch_normalize_conf: {}
energy_extract: null
energy_extract_conf: {}
energy_normalize: null
energy_normalize_conf: {}
required:
- output_dir
- token_list
version: '202209'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
mrkusypl/Nitrodolski
|
mrkusypl
| 2023-08-07T02:59:13Z | 0 | 0 | null |
[
"pl",
"region:us"
] | null | 2023-07-27T10:37:42Z |
---
language:
- pl
---
<center>
<img src="https://cdn.discordapp.com/attachments/1134073942835986442/1134073943100248064/Major-Suchodolski-prokuratura-wszczela-sledztwo-w-sprawie-smierci-patostreamera_article_north.png"></img>
<h1>Major Suchodolski (RVC v2) (Mangio Crepe 64) (250 Epochs)</h1>
**Model by:** kusy <br/>
**Voice Actor:** Wojciech "Major" Suchodolski <br/>
**Dataset:** 00:16:44 <br/>
<audio controls>
<source src="https://cdn.discordapp.com/attachments/1134073942835986442/1134073976491081799/example.mp3" type="audio/mpeg">
</audio><br />
<audio controls>
<source src="https://cdn.discordapp.com/attachments/1134073942835986442/1137932924612784178/gadanie.wav" type="audio/wav">
</audio>
<a href="https://huggingface.co/mrkusypl/Nitrodolski/resolve/main/Nitrodolski%20%5B250%20epoch%20%2B%20RVC%20v2%5D.zip">Download or copy the link</a>
</center>
|
mrkusypl/MexicanoTV
|
mrkusypl
| 2023-08-07T02:57:15Z | 0 | 0 | null |
[
"pl",
"region:us"
] | null | 2023-08-01T20:57:37Z |
---
language:
- pl
---
<center>
<img src="https://cdn.discordapp.com/attachments/1136043395123515465/1136043395928825957/comment_7oiVx1SlO3f8Ub44Vb0718v2vZin7XUk.png"></img>
<h1>MexicanoTV (RVC v2) (Mangio Crepe 64) (400 Epochs)</h1>
**Model by:** kusy <br/>
**Voice Actor:** Jarosław Andrzejewski <br/>
**Dataset:** 00:17:40 <br/>
<audio controls>
<source src="https://cdn.discordapp.com/attachments/1136043395123515465/1137050343440650341/example.mp3" type="audio/mpeg">
</audio><br />
<audio controls>
<source src="https://cdn.discordapp.com/attachments/1136043395123515465/1137932262139248741/gadanie.wav" type="audio/wav">
</audio>
<a href="https://huggingface.co/mrkusypl/MexicanoTV/resolve/main/MexicanoTV%20%5B400%20epoch%20%2B%20RVC%20v2%5D.zip">Download or copy the link</a>
</center>
|
mrkusypl/Kononowicz
|
mrkusypl
| 2023-08-07T02:55:42Z | 0 | 0 | null |
[
"pl",
"region:us"
] | null | 2023-07-26T16:20:00Z |
---
language:
- pl
---
<center>
<img src="https://wiez.pl/wp-content/uploads/2022/10/krzysztof-kononowicz-1-1-1408x1000.jpg"></img>
<h1>Krzysztof Kononowicz (RVC v2) (Mangio Crepe 64) (300 Epochs)</h1>
**Model by:** kusy <br/>
**Voice Actor:** Krzysztof Kononowicz <br/>
**Dataset:** 00:19:10 <br/>
<audio controls>
<source src="https://cdn.discordapp.com/attachments/1133799046327316592/1137482939828027392/example.mp3" type="audio/mpeg">
</audio><br />
<audio controls>
<source src="https://cdn.discordapp.com/attachments/1133799046327316592/1137929852339634266/gadanie.wav" type="audio/wav">
</audio>
<a href="https://huggingface.co/mrkusypl/Kononowicz/blob/main/Kononowicz%20%5B300%20epoch%20%2B%20RVC%20v2%5D.zip">Download or copy the link</a>
</center>
|
Yacong/lora-trained-xl
|
Yacong
| 2023-08-07T02:50:49Z | 3 | 1 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-08-07T01:33:38Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks dog
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - Yacong/lora-trained-xl
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
AmelieSchreiber/esm2_t6_8M_UR50D_LoRA_RNA-binding
|
AmelieSchreiber
| 2023-08-07T02:34:08Z | 4 | 1 |
peft
|
[
"peft",
"transformers",
"biology",
"esm",
"esm2",
"protein",
"protein language model",
"en",
"license:mit",
"region:us"
] | null | 2023-08-07T00:12:16Z |
---
library_name: peft
license: mit
language:
- en
tags:
- transformers
- biology
- esm
- esm2
- protein
- protein language model
---
# ESM-2 RNA Binding Site LoRA
This is a Parameter Efficient Fine Tuning (PEFT) Low Rank Adaptation (LoRA) of
the [esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) model for the (binary) token classification task of
predicting RNA binding sites of proteins. The Github with the training script and conda env YAML can be
[found here](https://github.com/Amelie-Schreiber/esm2_LoRA_binding_sites/tree/main). You can also find a version of this model
that was fine-tuned without LoRA [here](https://huggingface.co/AmelieSchreiber/esm2_t6_8M_UR50D_rna_binding_site_predictor).
## Training procedure
This is a Low Rank Adaptation (LoRA) of `esm2_t6_8M_UR50D`,
trained on `166` protein sequences in the [RNA binding sites dataset](https://huggingface.co/datasets/AmelieSchreiber/data_of_protein-rna_binding_sites)
using a `75/25` train/test split. It achieves an evaluation loss of `0.1791934072971344`.
### Framework versions
- PEFT 0.4.0
## Using the Model
To use, try running:
```python
from transformers import AutoModelForTokenClassification, AutoTokenizer
from peft import PeftModel
import torch
# Path to the saved LoRA model
model_path = "AmelieSchreiber/esm2_t6_8M_UR50D_LoRA_RNA-binding"
# ESM2 base model
base_model_path = "facebook/esm2_t6_8M_UR50D"
# Load the model
base_model = AutoModelForTokenClassification.from_pretrained(base_model_path)
loaded_model = PeftModel.from_pretrained(base_model, model_path)
# Ensure the model is in evaluation mode
loaded_model.eval()
# Load the tokenizer
loaded_tokenizer = AutoTokenizer.from_pretrained(base_model_path)
# Protein sequence for inference
protein_sequence = "MAVPETRPNHTIYINNLNEKIKKDELKKSLHAIFSRFGQILDILVSRSLKMRGQAFVIFKEVSSATNALRSMQGFPFYDKPMRIQYAKTDSDIIAKMKGT" # Replace with your actual sequence
# Tokenize the sequence
inputs = loaded_tokenizer(protein_sequence, return_tensors="pt", truncation=True, max_length=1024, padding='max_length')
# Run the model
with torch.no_grad():
logits = loaded_model(**inputs).logits
# Get predictions
tokens = loaded_tokenizer.convert_ids_to_tokens(inputs["input_ids"][0]) # Convert input ids back to tokens
predictions = torch.argmax(logits, dim=2)
# Define labels
id2label = {
0: "No binding site",
1: "Binding site"
}
# Print the predicted labels for each token
for token, prediction in zip(tokens, predictions[0].numpy()):
if token not in ['<pad>', '<cls>', '<eos>']:
print((token, id2label[prediction]))
```
|
dai1If/q-FrozenLake-v1-4x4-noSlippery
|
dai1If
| 2023-08-07T02:22:05Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-07T02:22:01Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="dai1If/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
gwlms/t5-efficient-large-dewiki-v1
|
gwlms
| 2023-08-07T01:50:15Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"de",
"dataset:gwlms/dewiki-20230701-chunks",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-25T12:10:45Z |
---
license: mit
datasets:
- gwlms/dewiki-20230701-chunks
language:
- de
---
# German Wikipedia LMs
Coming soon!
# Acknowledgements
Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC).
Many Thanks for providing access to the TPUs ❤️
|
Baronco98/Sudoku-Number-Classifier
|
Baronco98
| 2023-08-07T01:18:56Z | 2 | 0 |
keras
|
[
"keras",
"en",
"dataset:mnist",
"license:apache-2.0",
"region:us"
] | null | 2023-08-07T00:25:58Z |
---
license: apache-2.0
datasets:
- mnist
language:
- en
metrics:
- accuracy
library_name: keras
---
# Description
This model is a convolutional neural network built with transfer learning using the pre-trained model 'VGG16.' The 'block5_conv1' layer is retrained, and a final dense layer with 128 neurons is added.
The model will be used as a preliminary step in solving Sudokus through linear programming. Model It is responsible for classifying the content of each sudoku cell:
- class_0: empty cell
- class_1: cell contains the number 1
- class_2: cell contains the number 2
- class_3: cell contains the number 3
- class_4: cell contains the number 4
- class_5: cell contains the number 5
- class_6: cell contains the number 6
- class_7: cell contains the number 7
- class_8: cell contains the number 8
- class_9: cell contains the number 9
The dataset is constructed with balanced classes using images from the famous "MNIST digits classification" dataset, as well as images of numbers written digitally.
# Dataset schema
The image size it is 28x28 pixels. After applying data augmentation to the dataset, the total number of images is as follows:
- Training images: 5,600
- Validation images: 2,400
- Test images: 2,000
Test Accuracy: 0.9810
# Other validations:
An initial validation is performed. It remains pending to increase the size of the validations to understand the reliability of the mode
<div style="text-align: center;">
<img src="https://i.imgur.com/kdj9udt.jpg" width="300">
</div>
</div>
The results of the inference are as follows:
<div style="text-align: center;">
<img src="https://i.imgur.com/U2MJzH6.jpg" width="500">
</div>
|
taohoang/speecht5_finetuned_fleurs_en_us
|
taohoang
| 2023-08-07T01:18:29Z | 83 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:google/fleurs",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2023-08-07T01:04:34Z |
---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- google/fleurs
model-index:
- name: speecht5_finetuned_fleurs_en_us
results: []
pipeline_tag: text-to-speech
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_fleurs_en_us
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the google/fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4831
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 54
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.719 | 0.33 | 9 | 0.5634 |
| 0.5994 | 0.67 | 18 | 0.5290 |
| 0.584 | 1.0 | 27 | 0.4924 |
| 0.5589 | 1.33 | 36 | 0.4828 |
| 0.5747 | 1.67 | 45 | 0.4848 |
| 0.5904 | 2.0 | 54 | 0.4831 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
nhat117/checkpoint-7500-dica-long-llama-3b-sft
|
nhat117
| 2023-08-07T01:04:56Z | 4 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-01T07:55:56Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
xiangxiang/chatglm2-6b-WaJiaBank
|
xiangxiang
| 2023-08-07T00:55:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"chatglm",
"feature-extraction",
"custom_code",
"region:us"
] |
feature-extraction
| 2023-08-04T09:57:48Z |
## 模型介绍
ChatGLM2-6B 是清华开源中英双语对话模型 ChatGLM-6B 的第二代版本，具有模型对话流畅、部署门槛较低等众多优秀特性，ChatGLM2-6B 使用了 GLM 的混合目标函数上下文长度（Context Length）由 ChatGLM-6B 的 2K 扩展到了 32K，基于 Multi-Query Attention 技术，ChatGLM2-6B 有更高效的推理速度和更低的显存占用，推理速度相比初代提升了 42%，INT4 量化下，6G 显存支持的对话长度由 1K 提升到了 8K
**chatglm2-6b-WaJiaBank** 是基于清华 chatglm2-6b 进行量化+轻量微调，使用数据为网络公开数据。当前使用的数据量相对较少，模型泛化能力还需进一步提升。
#### 优化方向：
- 数据增强
- 性能调优
- 模型参数
## 调用方法
```python
from transformers import AutoTokenizer,AutoConfig, AutoModel, BitsAndBytesConfig
tokenizer = AutoTokenizer.from_pretrained("xiangxiang/chatglm2-6b-WaJiaBank", trust_remote_code=True)
model = AutoModel.from_pretrained("xiangxiang/chatglm2-6b-WaJiaBank", trust_remote_code=True).float() ## GPU cuda
```
提高模型推理速度，可以参考ChatGLM2-6B多卡部署方式
```python
from utils import load_model_on_gpus
model = load_model_on_gpus("THUDM/chatglm2-6b", num_gpus=2)
```
## 参考链接
https://github.com/THUDM/ChatGLM2-6B
|
brunoboat/Pixelcopter-PLE-v4
|
brunoboat
| 2023-08-07T00:48:34Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-07T00:48:32Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v4
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 10.50 +/- 11.24
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
brunoboat/Pixelcopter-PLE-v3
|
brunoboat
| 2023-08-07T00:42:31Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-07T00:42:27Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 43.20 +/- 35.30
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Yacong/my_dreambooth_out_dir
|
Yacong
| 2023-08-07T00:23:49Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2",
"base_model:finetune:stabilityai/stable-diffusion-2",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-06T15:09:47Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - Yacong/my_dreambooth_out_dir
This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
naasirfar/distilbert-base-uncased-finetuned-emotion
|
naasirfar
| 2023-08-06T23:52:39Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-06T23:10:18Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9295
- name: F1
type: f1
value: 0.9294307352150123
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2137
- Accuracy: 0.9295
- F1: 0.9294
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8048 | 1.0 | 250 | 0.3007 | 0.908 | 0.9047 |
| 0.2455 | 2.0 | 500 | 0.2137 | 0.9295 | 0.9294 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.0.1+cu118
- Datasets 1.16.1
- Tokenizers 0.13.3
|
brunoboat/Pixelcopter-PLE-v2
|
brunoboat
| 2023-08-06T23:49:26Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T23:49:23Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 25.00 +/- 21.79
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
harshV27/my-falcon-7b
|
harshV27
| 2023-08-06T23:04:54Z | 0 | 0 |
peft
|
[
"peft",
"pytorch",
"falcon",
"custom_code",
"region:us"
] | null | 2023-08-06T14:37:51Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
jmoney54378256438905/cybershart-temp
|
jmoney54378256438905
| 2023-08-06T23:00:25Z | 0 | 0 | null |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-08-06T22:59:55Z |
---
license: cc-by-nc-sa-4.0
---
|
ailabturkiye/ToronKaracaoglu
|
ailabturkiye
| 2023-08-06T22:58:14Z | 0 | 0 | null |
[
"tr",
"license:openrail",
"region:us"
] | null | 2023-08-06T22:30:23Z |
---
license: openrail
language:
- tr
---
|
Beniuv/Unit3SpaceInvaders
|
Beniuv
| 2023-08-06T22:57:17Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T22:56:42Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 565.00 +/- 135.46
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Beniuv -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Beniuv -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Beniuv
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
joelniklaus/legal-swiss-roberta-base
|
joelniklaus
| 2023-08-06T22:56:56Z | 68 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"multilingual",
"de",
"fr",
"it",
"arxiv:2306.02069",
"arxiv:2306.09237",
"arxiv:2301.13126",
"arxiv:2110.00976",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-12-28T00:26:09Z |
---
license: cc
language:
- multilingual
- de
- fr
- it
tags:
- multilingual
---
# Model Card for joelito/legal-swiss-roberta-base
This model is a multilingual model pretrained on legal data. It is based on XLM-R ([base](https://huggingface.co/xlm-roberta-base) and [large](https://huggingface.co/xlm-roberta-large)). For pretraining we used [Multi Legal Pile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile) ([Niklaus et al. 2023](https://arxiv.org/abs/2306.02069?utm_source=tldrai)), a multilingual dataset from various legal sources covering 24 languages.
## Model Details
### Model Description
- **Developed by:** Joel Niklaus: [huggingface](https://huggingface.co/joelito); [email](mailto:joel.niklaus.2@bfh.ch)
- **Model type:** Transformer-based language model (RoBERTa)
- **Language(s) (NLP):** de, fr, it
- **License:** CC BY-SA
## Uses
### Direct Use and Downstream Use
You can utilize the raw model for masked language modeling since we did not perform next sentence prediction. However, its main purpose is to be fine-tuned for downstream tasks.
It's important to note that this model is primarily designed for fine-tuning on tasks that rely on the entire sentence, potentially with masked elements, to make decisions. Examples of such tasks include sequence classification, token classification, or question answering. For text generation tasks, models like GPT-2 are more suitable.
Additionally, the model is specifically trained on legal data, aiming to deliver strong performance in that domain. Its performance may vary when applied to non-legal data.
### Out-of-Scope Use
For tasks such as text generation you should look at model like GPT2.
The model should not be used to intentionally create hostile or alienating environments for people. The model was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out-of-scope for the abilities of this model.
## Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
## How to Get Started with the Model
See [huggingface tutorials](https://huggingface.co/learn/nlp-course/chapter7/1?fw=pt). For masked word prediction see [this tutorial](https://huggingface.co/tasks/fill-mask).
## Training Details
This model was pretrained on [Multi Legal Pile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile) ([Niklaus et al. 2023](https://arxiv.org/abs/2306.02069?utm_source=tldrai)).
Our pretraining procedure includes the following key steps:
(a) Warm-starting: We initialize our models from the original XLM-R checkpoints ([base](https://huggingface.co/xlm-roberta-base) and [large](https://huggingface.co/xlm-roberta-large)) of [Conneau et al. (2019)](https://proceedings.neurips.cc/paper/2019/file/c04c19c2c2474dbf5f7ac4372c5b9af1-Paper.pdf) to benefit from a well-trained base.
(b) Tokenization: We train a new tokenizer of 128K BPEs to cover legal language better. However, we reuse the original XLM-R embeddings for lexically overlapping tokens and use random embeddings for the rest.
(c) Pretraining: We continue pretraining on Multi Legal Pile with batches of 512 samples for an additional 1M/500K steps for the base/large model. We use warm-up steps, a linearly increasing learning rate, and cosine decay scheduling. During the warm-up phase, only the embeddings are updated, and a higher masking rate and percentage of predictions based on masked tokens are used compared to [Devlin et al. (2019)](https://aclanthology.org/N19-1423).
(d) Sentence Sampling: We employ a sentence sampler with exponential smoothing to handle disparate token proportions across cantons and languages, preserving per-canton and language capacity.
(e) Mixed Cased Models: Our models cover both upper- and lowercase letters, similar to recently developed large PLMs.
(f) Long Context Training: To account for long contexts in legal documents, we train the base-size multilingual model on long contexts with windowed attention. This variant, named Legal-Swiss-LF-base, uses a 15% masking probability, increased learning rate, and similar settings to small-context models.
### Training Data
This model was pretrained on [Multi Legal Pile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile) ([Niklaus et al. 2023](https://arxiv.org/abs/2306.02069?utm_source=tldrai)).
#### Preprocessing
For further details see [Niklaus et al. 2023](https://arxiv.org/abs/2306.02069?utm_source=tldrai)
#### Training Hyperparameters
- batche size: 512 samples
- Number of steps: 1M/500K for the base/large model
- Warm-up steps for the first 5\% of the total training steps
- Learning rate: (linearly increasing up to) 1e-4
- Word masking: increased 20/30\% masking rate for base/large models respectively
## Evaluation
We compare joelito/legal-swiss-roberta-base with the other multilingual models.
The results are based on the text classification tasks presented in [Niklaus et al. (2023)](https://arxiv.org/abs/2306.09237) which are part of [LEXTREME](https://huggingface.co/datasets/joelito/lextreme).
We provide the arithmetic mean over three seeds (1, 2, 3) based on the macro-F1-score on the test set.
The highest values are in bold.
| \_name_or_path | SCP-BC | SCP-BF | SCP-CC | SCP-CF | SJPXL-C | SJPXL-F | SLAP-SC | SLAP-SF |
| :------------------------------------------------------------------------------------------------------ | :-------- | :-------- | :-------- | :-------- | :-------- | :-------- | :------- | :-------- |
| [ZurichNLP/swissbert-xlm-vocab](https://huggingface.co/ZurichNLP/swissbert-xlm-vocab) | 71.36 | 57.48 | 27.33 | 23.37 | 80.81 | 61.75 | 77.89 | 71.27 |
| [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) | 66.56 | 56.58 | 22.67 | 21.31 | 77.26 | 60.79 | 73.54 | 72.24 |
| [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) | 70.35 | 58.16 | 23.87 | 19.57 | 80.55 | 60.84 | 73.16 | 69.03 |
| [joelito/legal-swiss-longformer-base](https://huggingface.co/joelito/legal-swiss-longformer-base) | **73.25** | **60.06** | **28.68** | 24.39 | 87.46 | **65.23** | 83.84 | 77.96 |
| [joelito/legal-swiss-roberta-base](https://huggingface.co/joelito/legal-swiss-roberta-base) | 72.41 | 59.31 | 25.99 | 23.27 | 87.48 | 64.16 | **86.8** | **81.56** |
| [joelito/legal-swiss-roberta-large](https://huggingface.co/joelito/legal-swiss-roberta-large) | 70.95 | 57.59 | 27.86 | 23.48 | **88.33** | 62.92 | 82.1 | 78.62 |
| [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) | 67.29 | 56.56 | 24.23 | 14.9 | 79.52 | 58.29 | 63.03 | 67.57 |
| [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) | 72.01 | 57.59 | 22.93 | **25.18** | 79.41 | 60.89 | 67.64 | 74.13 |
| [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) | 68.55 | 58.48 | 25.66 | 21.52 | 80.98 | 61.45 | 79.3 | 74.47 |
| [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) | 69.5 | 58.15 | 27.9 | 22.05 | 82.19 | 61.24 | 81.09 | 71.82 |
For more detailed insights into the performance on downstream tasks, such as [LEXTREME](https://huggingface.co/datasets/joelito/lextreme) ([Niklaus et al. 2023](https://arxiv.org/abs/2301.13126)) or [LEXGLUE](https://huggingface.co/datasets/lex_glue) ([Chalkidis et al. 2021](https://arxiv.org/abs/2110.00976)), we refer to the results presented in Niklaus et al. (2023) [1](https://arxiv.org/abs/2306.02069), [2](https://arxiv.org/abs/2306.09237).
For further insights into the evaluation, we refer to the [trainer state](https://huggingface.co/joelito/legal-swiss-roberta-base/blob/main/last-checkpoint/trainer_state.json). Additional information is available in the [tensorboard](https://huggingface.co/joelito/legal-swiss-roberta-base/tensorboard).
### Model Architecture and Objective
It is a RoBERTa-based model. Run the following code to view the architecture:
```
from transformers import AutoModel
model = AutoModel.from_pretrained('joelito/legal-swiss-roberta-base')
print(model)
RobertaModel(
(embeddings): RobertaEmbeddings(
(word_embeddings): Embedding(128000, 768, padding_idx=0)
(position_embeddings): Embedding(514, 768, padding_idx=0)
(token_type_embeddings): Embedding(1, 768)
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): RobertaEncoder(
(layer): ModuleList(
(0-11): 12 x RobertaLayer(
(attention): RobertaAttention(
(self): RobertaSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): RobertaSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): RobertaIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): RobertaOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(pooler): RobertaPooler(
(dense): Linear(in_features=768, out_features=768, bias=True)
(activation): Tanh()
)
)
```
### Compute Infrastructure
Google TPU.
#### Hardware
Google TPU v3-8
#### Software
pytorch, transformers.
## Citation
```
@misc{rasiah2023scale,
title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation},
author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},
year={2023},
eprint={2306.09237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{Niklaus2023MultiLegalPileA6,
title={MultiLegalPile: A 689GB Multilingual Legal Corpus},
author={Joel Niklaus and Veton Matoshi and Matthias Sturmer and Ilias Chalkidis and Daniel E. Ho},
journal={ArXiv},
year={2023},
volume={abs/2306.02069}
}
```
## Model Card Authors
Joel Niklaus: [huggingface](https://huggingface.co/joelito); [email](mailto:joel.niklaus.2@bfh.ch)
Veton Matoshi: [huggingface](https://huggingface.co/kapllan); [email](mailto:msv3@bfh.ch)
## Model Card Contact
Joel Niklaus: [huggingface](https://huggingface.co/joelito); [email](mailto:joel.niklaus.2@bfh.ch)
Veton Matoshi: [huggingface](https://huggingface.co/kapllan); [email](mailto:msv3@bfh.ch)
|
joelniklaus/legal-swiss-roberta-large
|
joelniklaus
| 2023-08-06T22:56:51Z | 16 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"multilingual",
"de",
"fr",
"it",
"dataset:MultiLegalPile",
"dataset:LEXTREME",
"dataset:LEXGLUE",
"arxiv:2306.02069",
"arxiv:2306.09237",
"arxiv:2301.13126",
"arxiv:2110.00976",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-01-16T18:30:21Z |
---
language:
- multilingual
- de
- fr
- it
tags:
- multilingual
license: cc
datasets:
- MultiLegalPile
- LEXTREME
- LEXGLUE
---
# Model Card for joelito/legal-swiss-roberta-large
This model is a multilingual model pretrained on legal data. It is based on XLM-R ([base](https://huggingface.co/xlm-roberta-base) and [large](https://huggingface.co/xlm-roberta-large)). For pretraining we used [Multi Legal Pile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile) ([Niklaus et al. 2023](https://arxiv.org/abs/2306.02069?utm_source=tldrai)), a multilingual dataset from various legal sources covering 24 languages.
## Model Details
### Model Description
- **Developed by:** Joel Niklaus: [huggingface](https://huggingface.co/joelito); [email](mailto:joel.niklaus.2@bfh.ch)
- **Model type:** Transformer-based language model (RoBERTa)
- **Language(s) (NLP):** de, fr, it
- **License:** CC BY-SA
## Uses
### Direct Use and Downstream Use
You can utilize the raw model for masked language modeling since we did not perform next sentence prediction. However, its main purpose is to be fine-tuned for downstream tasks.
It's important to note that this model is primarily designed for fine-tuning on tasks that rely on the entire sentence, potentially with masked elements, to make decisions. Examples of such tasks include sequence classification, token classification, or question answering. For text generation tasks, models like GPT-2 are more suitable.
Additionally, the model is specifically trained on legal data, aiming to deliver strong performance in that domain. Its performance may vary when applied to non-legal data.
### Out-of-Scope Use
For tasks such as text generation you should look at model like GPT2.
The model should not be used to intentionally create hostile or alienating environments for people. The model was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out-of-scope for the abilities of this model.
## Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
## How to Get Started with the Model
See [huggingface tutorials](https://huggingface.co/learn/nlp-course/chapter7/1?fw=pt). For masked word prediction see [this tutorial](https://huggingface.co/tasks/fill-mask).
## Training Details
This model was pretrained on [Multi Legal Pile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile) ([Niklaus et al. 2023](https://arxiv.org/abs/2306.02069?utm_source=tldrai)).
Our pretraining procedure includes the following key steps:
(a) Warm-starting: We initialize our models from the original XLM-R checkpoints ([base](https://huggingface.co/xlm-roberta-base) and [large](https://huggingface.co/xlm-roberta-large)) of [Conneau et al. (2019)](https://proceedings.neurips.cc/paper/2019/file/c04c19c2c2474dbf5f7ac4372c5b9af1-Paper.pdf) to benefit from a well-trained base.
(b) Tokenization: We train a new tokenizer of 128K BPEs to cover legal language better. However, we reuse the original XLM-R embeddings for lexically overlapping tokens and use random embeddings for the rest.
(c) Pretraining: We continue pretraining on Multi Legal Pile with batches of 512 samples for an additional 1M/500K steps for the base/large model. We use warm-up steps, a linearly increasing learning rate, and cosine decay scheduling. During the warm-up phase, only the embeddings are updated, and a higher masking rate and percentage of predictions based on masked tokens are used compared to [Devlin et al. (2019)](https://aclanthology.org/N19-1423).
(d) Sentence Sampling: We employ a sentence sampler with exponential smoothing to handle disparate token proportions across cantons and languages, preserving per-canton and language capacity.
(e) Mixed Cased Models: Our models cover both upper- and lowercase letters, similar to recently developed large PLMs.
(f) Long Context Training: To account for long contexts in legal documents, we train the base-size multilingual model on long contexts with windowed attention. This variant, named Legal-Swiss-LF-base, uses a 15% masking probability, increased learning rate, and similar settings to small-context models.
### Training Data
This model was pretrained on [Multi Legal Pile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile) ([Niklaus et al. 2023](https://arxiv.org/abs/2306.02069?utm_source=tldrai)).
#### Preprocessing
For further details see [Niklaus et al. 2023](https://arxiv.org/abs/2306.02069?utm_source=tldrai)
#### Training Hyperparameters
- batche size: 512 samples
- Number of steps: 1M/500K for the base/large model
- Warm-up steps for the first 5\% of the total training steps
- Learning rate: (linearly increasing up to) 1e-4
- Word masking: increased 20/30\% masking rate for base/large models respectively
## Evaluation
We compare joelito/legal-swiss-roberta-large with the other multilingual models.
The results are based on the text classification tasks presented in [Niklaus et al. (2023)](https://arxiv.org/abs/2306.09237) which are part of [LEXTREME](https://huggingface.co/datasets/joelito/lextreme).
We provide the arithmetic mean over three seeds (1, 2, 3) based on the macro-F1-score on the test set.
The highest values are in bold.
| _name_or_path | SCP-BC | SCP-BF | SCP-CC | SCP-CF | SJPXL-C | SJPXL-F | SLAP-SC | SLAP-SF |
|:--------------------------------------------------------------------------------------------------------|:----------|:----------|:----------|:----------|:----------|:----------|:----------|:----------|
| [ZurichNLP/swissbert-xlm-vocab](https://huggingface.co/ZurichNLP/swissbert-xlm-vocab) | 71.36 | 57.48 | 27.33 | 23.37 | 80.81 | 61.75 | 77.89 | 71.27 |
| [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) | 66.56 | 56.58 | 22.67 | 21.31 | 77.26 | 60.79 | 73.54 | 72.24 |
| [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) | 70.35 | 58.16 | 23.87 | 19.57 | 80.55 | 60.84 | 73.16 | 69.03 |
| [joelito/legal-swiss-longformer-base](https://huggingface.co/joelito/legal-swiss-longformer-base) | **73.25** | **60.06** | **28.68** | 24.39 | 87.46 | **65.23** | 83.84 | 77.96 |
| [joelito/legal-swiss-roberta-base](https://huggingface.co/joelito/legal-swiss-roberta-base) | 72.41 | 59.31 | 25.99 | 23.27 | 87.48 | 64.16 | **86.8** | **81.56** |
| [joelito/legal-swiss-roberta-large](https://huggingface.co/joelito/legal-swiss-roberta-large) | 70.95 | 57.59 | 27.86 | 23.48 | **88.33** | 62.92 | 82.1 | 78.62 |
| [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) | 67.29 | 56.56 | 24.23 | 14.9 | 79.52 | 58.29 | 63.03 | 67.57 |
| [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) | 72.01 | 57.59 | 22.93 | **25.18** | 79.41 | 60.89 | 67.64 | 74.13 |
| [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) | 68.55 | 58.48 | 25.66 | 21.52 | 80.98 | 61.45 | 79.3 | 74.47 |
| [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) | 69.5 | 58.15 | 27.9 | 22.05 | 82.19 | 61.24 | 81.09 | 71.82 |
For more detailed insights into the performance on downstream tasks, such as [LEXTREME](https://huggingface.co/datasets/joelito/lextreme) ([Niklaus et al. 2023](https://arxiv.org/abs/2301.13126)) or [LEXGLUE](https://huggingface.co/datasets/lex_glue) ([Chalkidis et al. 2021](https://arxiv.org/abs/2110.00976)), we refer to the results presented in Niklaus et al. (2023) [1](https://arxiv.org/abs/2306.02069), [2](https://arxiv.org/abs/2306.09237).
For further insights into the evaluation, we refer to the [trainer state](https://huggingface.co/joelito/legal-xlm-roberta-large/blob/main/last-checkpoint/trainer_state.json). Additional information is available in the [tensorboard](https://huggingface.co/joelito/legal-xlm-roberta-large/tensorboard).
### Model Architecture and Objective
It is a RoBERTa-based model. Run the following code to view the architecture:
```
from transformers import AutoModel
model = AutoModel.from_pretrained('model_identifier')
print(model)
RobertaModel(
(embeddings): RobertaEmbeddings(
(word_embeddings): Embedding(128000, 1024, padding_idx=0)
(position_embeddings): Embedding(514, 1024, padding_idx=0)
(token_type_embeddings): Embedding(1, 1024)
(LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): RobertaEncoder(
(layer): ModuleList(
(0-23): 24 x RobertaLayer(
(attention): RobertaAttention(
(self): RobertaSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): RobertaSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): RobertaIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): RobertaOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(pooler): RobertaPooler(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(activation): Tanh()
)
)
```
### Compute Infrastructure
Google TPU.
#### Hardware
Google TPU v3-8
#### Software
pytorch, transformers.
## Citation
```
@misc{rasiah2023scale,
title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation},
author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},
year={2023},
eprint={2306.09237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{Niklaus2023MultiLegalPileA6,
title={MultiLegalPile: A 689GB Multilingual Legal Corpus},
author={Joel Niklaus and Veton Matoshi and Matthias Sturmer and Ilias Chalkidis and Daniel E. Ho},
journal={ArXiv},
year={2023},
volume={abs/2306.02069}
}
```
## Model Card Authors
Joel Niklaus: [huggingface](https://huggingface.co/joelito); [email](mailto:joel.niklaus.2@bfh.ch)
Veton Matoshi: [huggingface](https://huggingface.co/kapllan); [email](mailto:msv3@bfh.ch)
## Model Card Contact
Joel Niklaus: [huggingface](https://huggingface.co/joelito); [email](mailto:joel.niklaus.2@bfh.ch)
Veton Matoshi: [huggingface](https://huggingface.co/kapllan); [email](mailto:msv3@bfh.ch)
|
joelniklaus/legal-english-longformer-base
|
joelniklaus
| 2023-08-06T22:55:40Z | 0 | 2 | null |
[
"en",
"dataset:MultiLegalPile",
"dataset:LEXTREME",
"dataset:LEXGLUE",
"arxiv:2306.02069",
"arxiv:2301.13126",
"arxiv:2110.00976",
"arxiv:2306.09237",
"license:cc",
"region:us"
] | null | 2023-04-27T06:52:14Z |
---
license: cc
language:
- en
datasets:
- MultiLegalPile
- LEXTREME
- LEXGLUE
---
# Model Card for joelito/legal-english-longformer-base
This model is a multilingual model pretrained on legal data. It is based on XLM-R ([base](https://huggingface.co/xlm-roberta-base) and [large](https://huggingface.co/xlm-roberta-large)). For pretraining we used [Multi Legal Pile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile) ([Niklaus et al. 2023](https://arxiv.org/abs/2306.02069)), a multilingual dataset from various legal sources covering 24 languages.
## Model Details
### Model Description
- **Developed by:** Joel Niklaus: [huggingface](https://huggingface.co/joelito); [email](mailto:joel.niklaus.2@bfh.ch)
- **Model type:** Transformer-based language model (Longformer)
- **Language(s) (NLP):** en
- **License:** CC BY-SA
## Uses
### Direct Use and Downstream Use
You can utilize the raw model for masked language modeling since we did not perform next sentence prediction. However, its main purpose is to be fine-tuned for downstream tasks.
It's important to note that this model is primarily designed for fine-tuning on tasks that rely on the entire sentence, potentially with masked elements, to make decisions. Examples of such tasks include sequence classification, token classification, or question answering. For text generation tasks, models like GPT-2 are more suitable.
Additionally, the model is specifically trained on legal data, aiming to deliver strong performance in that domain. Its performance may vary when applied to non-legal data.
### Out-of-Scope Use
For tasks such as text generation you should look at model like GPT2.
The model should not be used to intentionally create hostile or alienating environments for people. The model was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out-of-scope for the abilities of this model.
## Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
## How to Get Started with the Model
See [huggingface tutorials](https://huggingface.co/learn/nlp-course/chapter7/1?fw=pt). For masked word prediction see [this tutorial](https://huggingface.co/tasks/fill-mask).
## Training Details
This model was pretrained on [Multi Legal Pile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile) ([Niklaus et al. 2023](https://arxiv.org/abs/2306.02069?utm_source=tldrai)).
Our pretraining procedure includes the following key steps:
(a) Warm-starting: We initialize our models from the original XLM-R checkpoints ([base](https://huggingface.co/xlm-roberta-base) and [large](https://huggingface.co/xlm-roberta-large)) of [Conneau et al. (2019)](https://proceedings.neurips.cc/paper/2019/file/c04c19c2c2474dbf5f7ac4372c5b9af1-Paper.pdf) to benefit from a well-trained base.
(b) Tokenization: We train a new tokenizer of 128K BPEs to cover legal language better. However, we reuse the original XLM-R embeddings for lexically overlapping tokens and use random embeddings for the rest.
(c) Pretraining: We continue pretraining on Multi Legal Pile with batches of 512 samples for an additional 1M/500K steps for the base/large model. We use warm-up steps, a linearly increasing learning rate, and cosine decay scheduling. During the warm-up phase, only the embeddings are updated, and a higher masking rate and percentage of predictions based on masked tokens are used compared to [Devlin et al. (2019)](https://aclanthology.org/N19-1423).
(d) Sentence Sampling: We employ a sentence sampler with exponential smoothing to handle disparate token proportions across cantons and languages, preserving per-canton and language capacity.
(e) Mixed Cased Models: Our models cover both upper- and lowercase letters, similar to recently developed large PLMs.
(f) Long Context Training: To account for long contexts in legal documents, we train the base-size multilingual model on long contexts with windowed attention. This variant, named Legal-Swiss-LF-base, uses a 15% masking probability, increased learning rate, and similar settings to small-context models.
### Training Data
This model was pretrained on [Multi Legal Pile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile) ([Niklaus et al. 2023](https://arxiv.org/abs/2306.02069?utm_source=tldrai)).
#### Preprocessing
For further details see [Niklaus et al. 2023](https://arxiv.org/abs/2306.02069?utm_source=tldrai)
#### Training Hyperparameters
- batche size: 512 samples
- Number of steps: 1M/500K for the base/large model
- Warm-up steps for the first 5\% of the total training steps
- Learning rate: (linearly increasing up to) 1e-4
- Word masking: increased 20/30\% masking rate for base/large models respectively
## Evaluation
For performance on downstream tasks, such as [LEXTREME](https://huggingface.co/datasets/joelito/lextreme) ([Niklaus et al. 2023](https://arxiv.org/abs/2301.13126)) or [LEXGLUE](https://huggingface.co/datasets/lex_glue) ([Chalkidis et al. 2021](https://arxiv.org/abs/2110.00976)), we refer to the results presented in Niklaus et al. (2023) [1](https://arxiv.org/abs/2306.02069), [2](https://arxiv.org/abs/2306.09237).
### Model Architecture and Objective
It is a RoBERTa-based model. Run the following code to view the architecture:
```
from transformers import AutoModel
model = AutoModel.from_pretrained('joelito/legal-english-longformer-base')
print(model)
LongformerModel(
(embeddings): LongformerEmbeddings(
(word_embeddings): Embedding(128000, 768, padding_idx=0)
(position_embeddings): Embedding(4098, 768, padding_idx=0)
(token_type_embeddings): Embedding(1, 768)
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): LongformerEncoder(
(layer): ModuleList(
(0-11): 12 x LongformerLayer(
(attention): LongformerAttention(
(self): LongformerSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(query_global): Linear(in_features=768, out_features=768, bias=True)
(key_global): Linear(in_features=768, out_features=768, bias=True)
(value_global): Linear(in_features=768, out_features=768, bias=True)
)
(output): LongformerSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): LongformerIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): LongformerOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(pooler): LongformerPooler(
(dense): Linear(in_features=768, out_features=768, bias=True)
(activation): Tanh()
)
)
```
### Compute Infrastructure
Google TPU.
#### Hardware
Google TPU v3-8
#### Software
pytorch, transformers.
## Citation
```
@article{Niklaus2023MultiLegalPileA6,
title={MultiLegalPile: A 689GB Multilingual Legal Corpus},
author={Joel Niklaus and Veton Matoshi and Matthias Sturmer and Ilias Chalkidis and Daniel E. Ho},
journal={ArXiv},
year={2023},
volume={abs/2306.02069}
}
```
## Model Card Authors
Joel Niklaus: [huggingface](https://huggingface.co/joelito); [email](mailto:joel.niklaus.2@bfh.ch)
Veton Matoshi: [huggingface](https://huggingface.co/kapllan); [email](mailto:msv3@bfh.ch)
## Model Card Contact
Joel Niklaus: [huggingface](https://huggingface.co/joelito); [email](mailto:joel.niklaus.2@bfh.ch)
Veton Matoshi: [huggingface](https://huggingface.co/kapllan); [email](mailto:msv3@bfh.ch)
|
HydraLM/Nous-Hermes-llama-2-7b_7b_cluster030_partitioned_v3_standardized_030
|
HydraLM
| 2023-08-06T22:55:06Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T17:53:43Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
smd142/model
|
smd142
| 2023-08-06T22:53:17Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T06:31:01Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
HydraLM/Nous-Hermes-llama-2-7b_7b_cluster027_partitioned_v3_standardized_027
|
HydraLM
| 2023-08-06T22:32:44Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T05:48:21Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
ailabturkiye/RizaTuzun
|
ailabturkiye
| 2023-08-06T22:28:52Z | 0 | 0 | null |
[
"tr",
"license:openrail",
"region:us"
] | null | 2023-08-06T22:24:13Z |
---
license: openrail
language:
- tr
---
|
brunoboat/Pixelcopter-PLE-v1
|
brunoboat
| 2023-08-06T22:22:51Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T22:22:49Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 14.50 +/- 10.47
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
HydraLM/Nous-Hermes-llama-2-7b_7b_cluster021_partitioned_v3_standardized_021
|
HydraLM
| 2023-08-06T22:00:31Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T06:04:49Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
spicecloud/bert-yelp-local
|
spicecloud
| 2023-08-06T21:40:56Z | 126 | 0 |
transformers
|
[
"transformers",
"pytorch",
"coreml",
"safetensors",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-06T21:40:25Z |
---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
the Hugging Face team.
## Model description
BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
the entire masked sentence through the model and has to predict the masked words. This is different from traditional
recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the
sentence.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
predict if the two sentences were following each other or not.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Model variations
BERT has originally been released in base and large variations, for cased and uncased input text. The uncased models also strips out an accent markers.
Chinese and multilingual uncased and cased versions followed shortly after.
Modified preprocessing with whole word masking has replaced subpiece masking in a following work, with the release of two models.
Other 24 smaller models are released afterward.
The detailed release history can be found on the [google-research/bert readme](https://github.com/google-research/bert/blob/master/README.md) on github.
| Model | #params | Language |
|------------------------|--------------------------------|-------|
| [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) | 110M | English |
| [`bert-large-uncased`](https://huggingface.co/bert-large-uncased) | 340M | English | sub
| [`bert-base-cased`](https://huggingface.co/bert-base-cased) | 110M | English |
| [`bert-large-cased`](https://huggingface.co/bert-large-cased) | 340M | English |
| [`bert-base-chinese`](https://huggingface.co/bert-base-chinese) | 110M | Chinese |
| [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) | 110M | Multiple |
| [`bert-large-uncased-whole-word-masking`](https://huggingface.co/bert-large-uncased-whole-word-masking) | 340M | English |
| [`bert-large-cased-whole-word-masking`](https://huggingface.co/bert-large-cased-whole-word-masking) | 340M | English |
## Intended uses & limitations
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
fine-tuned versions of a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
'score': 0.1073106899857521,
'token': 4827,
'token_str': 'fashion'},
{'sequence': "[CLS] hello i'm a role model. [SEP]",
'score': 0.08774490654468536,
'token': 2535,
'token_str': 'role'},
{'sequence': "[CLS] hello i'm a new model. [SEP]",
'score': 0.05338378623127937,
'token': 2047,
'token_str': 'new'},
{'sequence': "[CLS] hello i'm a super model. [SEP]",
'score': 0.04667217284440994,
'token': 3565,
'token_str': 'super'},
{'sequence': "[CLS] hello i'm a fine model. [SEP]",
'score': 0.027095865458250046,
'token': 2986,
'token_str': 'fine'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("The man worked as a [MASK].")
[{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
'score': 0.09747550636529922,
'token': 10533,
'token_str': 'carpenter'},
{'sequence': '[CLS] the man worked as a waiter. [SEP]',
'score': 0.0523831807076931,
'token': 15610,
'token_str': 'waiter'},
{'sequence': '[CLS] the man worked as a barber. [SEP]',
'score': 0.04962705448269844,
'token': 13362,
'token_str': 'barber'},
{'sequence': '[CLS] the man worked as a mechanic. [SEP]',
'score': 0.03788609802722931,
'token': 15893,
'token_str': 'mechanic'},
{'sequence': '[CLS] the man worked as a salesman. [SEP]',
'score': 0.037680890411138535,
'token': 18968,
'token_str': 'salesman'}]
>>> unmasker("The woman worked as a [MASK].")
[{'sequence': '[CLS] the woman worked as a nurse. [SEP]',
'score': 0.21981462836265564,
'token': 6821,
'token_str': 'nurse'},
{'sequence': '[CLS] the woman worked as a waitress. [SEP]',
'score': 0.1597415804862976,
'token': 13877,
'token_str': 'waitress'},
{'sequence': '[CLS] the woman worked as a maid. [SEP]',
'score': 0.1154729500412941,
'token': 10850,
'token_str': 'maid'},
{'sequence': '[CLS] the woman worked as a prostitute. [SEP]',
'score': 0.037968918681144714,
'token': 19215,
'token_str': 'prostitute'},
{'sequence': '[CLS] the woman worked as a cook. [SEP]',
'score': 0.03042375110089779,
'token': 5660,
'token_str': 'cook'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
headers).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus, and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
### Pretraining
The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.
## Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
Glue test results:
| Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average |
|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
| | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 |
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1810-04805,
author = {Jacob Devlin and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805},
timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<a href="https://huggingface.co/exbert/?model=bert-base-uncased">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
HydraLM/Nous-Hermes-llama-2-7b_7b_cluster016_partitioned_v3_standardized_016
|
HydraLM
| 2023-08-06T21:36:47Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T06:20:06Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
Xillolxlbln/my_awesome_qa_model
|
Xillolxlbln
| 2023-08-06T21:33:09Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-04T21:00:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0252
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 125 | 3.0587 |
| No log | 2.0 | 250 | 2.1943 |
| No log | 3.0 | 375 | 2.0252 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
ailabturkiye/sehinsah2
|
ailabturkiye
| 2023-08-06T21:21:49Z | 0 | 0 | null |
[
"music",
"tr",
"license:openrail",
"region:us"
] | null | 2023-08-06T21:15:04Z |
---
license: openrail
language:
- tr
tags:
- music
---
Şehinşah'ın çıplak sesiyle yapılan ses modeli. Train ve dataset bana aittir.
|
pillocode/ppo-Huggy
|
pillocode
| 2023-08-06T21:21:46Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-06T21:21:42Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: pillocode/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
HydraLM/Nous-Hermes-llama-2-7b_7b_cluster012_partitioned_v3_standardized_012
|
HydraLM
| 2023-08-06T21:11:11Z | 5 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T17:52:36Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
HydraLM/Nous-Hermes-llama-2-7b_7b_cluster010_partitioned_v3_standardized_010
|
HydraLM
| 2023-08-06T21:01:19Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T05:53:11Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
LarryAIDraw/Doria_v1
|
LarryAIDraw
| 2023-08-06T20:59:38Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-06T20:52:22Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/123204/andrea-doria-azur-lane
|
LarryAIDraw/Patchi_V1
|
LarryAIDraw
| 2023-08-06T20:59:25Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-06T20:52:00Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/123345/skv-patchouli-knowledge-touhou-lora
|
LarryAIDraw/swimanis-v1-nai-resize
|
LarryAIDraw
| 2023-08-06T20:58:10Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-06T20:50:07Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/123679/anis-sparkling-summer-nikke
|
HydraLM/Nous-Hermes-llama-2-7b_7b_cluster07_partitioned_v3_standardized_07
|
HydraLM
| 2023-08-06T20:41:42Z | 4 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T05:53:20Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
li-ping/summary_llama_3_epoch_ver2_fix_wavedrom
|
li-ping
| 2023-08-06T20:38:39Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T20:07:37Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
HydraLM/Nous-Hermes-llama-2-7b_7b_cluster05_partitioned_v3_standardized_05
|
HydraLM
| 2023-08-06T20:29:53Z | 9 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T05:53:14Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
saaketh-j/llama-business
|
saaketh-j
| 2023-08-06T20:28:10Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T20:26:39Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
prompt = f"""
You are going to determine whether the description includes the business model. Don't use any prior knowledge, only base your answer off of what's given. It might not be explicitly stated but if it says "they sell in retailers" or "they sell to customers", it can be reasonably assumed that a B2C model is stated. If it says they "create software solutions" or "support companies", it is safe to assume they are B2B. If it says they are "the top defense contractor" or that they "create intelligence software for the FBI", it is reasonable to say they are B2G. However, if the information is very sparse or you are unsure, "No business model" is also a category to classify into. You should only classify into B2C, B2B, B2G, No business model. The response should be in sentence form with the class and reasoning ->:
<Description>: [{data_point["Description"]}]
<Answer>: {data_point["Answer"]}
"""
config = LoraConfig(
r=64,
lora_alpha=16,
lora_dropout = 0.1,
bias="none",
task_type = "CAUSAL_LM"
)
|
MattStammers/ppo-lunarlandercontinuous
|
MattStammers
| 2023-08-06T20:27:37Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T19:47:07Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 279.83 +/- 22.33
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
HydraLM/Nous-Hermes-llama-2-7b_7b_cluster04_partitioned_v3_standardized_04
|
HydraLM
| 2023-08-06T20:21:35Z | 5 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T05:53:17Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
CristoJV/q-FrozenLake-v1-4x4-noSlippery
|
CristoJV
| 2023-08-06T19:52:20Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T19:52:16Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="CristoJV/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
BauyrjanQ/whisper-kk-sp2n-b16-ms1600-s
|
BauyrjanQ
| 2023-08-06T19:46:25Z | 79 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-05T22:16:53Z |
---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-kk-sp2n-b16-ms1600-s
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-kk-sp2n-b16-ms1600-s
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2659
- Wer: 274.5556
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1600
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9302 | 0.18 | 800 | 0.3010 | 229.3416 |
| 0.83 | 0.37 | 1600 | 0.2659 | 274.5556 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
alexeynoskov/dqn-SpaceInvadersNoFrameskip-v4
|
alexeynoskov
| 2023-08-06T19:44:46Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T19:44:11Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 652.00 +/- 106.28
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga alexeynoskov -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga alexeynoskov -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga alexeynoskov
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
HydraLM/Nous-Hermes-llama-2-7b_7b_cluster02_partitioned_v3_standardized_02
|
HydraLM
| 2023-08-06T19:43:05Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T17:51:59Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
s3nh/WizardVicuna-Uncensored-3B-0719-GGML
|
s3nh
| 2023-08-06T19:22:33Z | 0 | 6 |
transformers
|
[
"transformers",
"text-generation",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-06T19:03:42Z |
---
license: openrail
language:
- en
pipeline_tag: text-generation
library_name: transformers
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/heegyu/WizardVicuna-Uncensored-3B-0719).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
Base Model: togethercomputer/RedPajama-INCITE-Base-3B-v1
Usage
```
### Human:
your instruction
### ASSISANT:
output will be generated and ended with <|endoftext|>
```
|
MattStammers/Bipedal_Walker_v3_Hardcore_Flat_Optimised
|
MattStammers
| 2023-08-06T19:15:39Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T19:14:56Z |
---
library_name: stable-baselines3
tags:
- BipedalWalker-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BipedalWalker-v3
type: BipedalWalker-v3
metrics:
- type: mean_reward
value: -85.95 +/- 18.79
name: mean_reward
verified: false
---
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ThuyNT03/xlm-roberta-base-finetuned-panx-all
|
ThuyNT03
| 2023-08-06T19:12:32Z | 88 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-06T18:49:24Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1764
- F1: 0.8572
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.297 | 1.0 | 835 | 0.1950 | 0.8093 |
| 0.1555 | 2.0 | 1670 | 0.1687 | 0.8455 |
| 0.1 | 3.0 | 2505 | 0.1764 | 0.8572 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
aivance/rebranding-to-aistrova
|
aivance
| 2023-08-06T19:10:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-08-06T19:08:54Z |
We're moving to https://huggingface.co/aistrova
|
gremlin97/CuteControlNet
|
gremlin97
| 2023-08-06T18:55:37Z | 0 | 0 | null |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-08-06T18:31:55Z |
---
license: cc-by-4.0
---
This repo contains a Control Net model trained on my custom dog dataset: [Dataset Link](https://huggingface.co/datasets/gremlin97/cute-controlnet). I have trained this model on 2 A100 GPU clusters using distributed training enabled through PyTorch Lightning API. It has been trained for 1 epoch for a duration of 4-5 hours. The model achieves good results. Custom Control-Net is specialized in dogs trained from scratch with the aforementioned custom dog dataset.
|
yosaku/finetuning-sentiment-model-3000-samples
|
yosaku
| 2023-08-06T18:47:37Z | 90 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-06T04:00:07Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.8675496688741722
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3196
- Accuracy: 0.8667
- F1: 0.8675
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
Peniis2/Airplane
|
Peniis2
| 2023-08-06T18:43:04Z | 0 | 0 | null |
[
"en",
"dataset:databricks/databricks-dolly-15k",
"region:us"
] | null | 2023-08-06T18:41:29Z |
---
datasets:
- databricks/databricks-dolly-15k
language:
- en
---
|
Surya-Teja-Menta/ppo-Huggy
|
Surya-Teja-Menta
| 2023-08-06T18:40:20Z | 6 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-06T18:40:14Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Surya-Teja-Menta/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ThuyNT03/xlm-roberta-base-finetuned-panx-de-fr
|
ThuyNT03
| 2023-08-06T18:37:02Z | 95 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-06T18:23:38Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1603
- F1: 0.8595
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 715 | 0.1777 | 0.8240 |
| No log | 2.0 | 1430 | 0.1603 | 0.8420 |
| No log | 3.0 | 2145 | 0.1603 | 0.8595 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
Lilsunx/llama2-qlora-finetunined-french
|
Lilsunx
| 2023-08-06T18:29:00Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T18:28:52Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
UHS/PPO_Bipedal_Walker_Flat_Optimised
|
UHS
| 2023-08-06T18:22:30Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T18:21:21Z |
---
library_name: stable-baselines3
tags:
- BipedalWalker-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BipedalWalker-v3
type: BipedalWalker-v3
metrics:
- type: mean_reward
value: 302.24 +/- 1.27
name: mean_reward
verified: false
---
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Javeria98/donut_final_REAL_DATA_04
|
Javeria98
| 2023-08-06T18:19:58Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-08-06T14:41:39Z |
---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut_final_REAL_DATA_04
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# donut_final_REAL_DATA_04
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
HasanErdin/ppo-Huggy
|
HasanErdin
| 2023-08-06T18:14:39Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-06T18:14:34Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: HasanErdin/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
textgain/allnli-GroNLP-bert-base-dutch-cased
|
textgain
| 2023-08-06T18:09:12Z | 553 | 3 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"nl",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-01-16T13:17:02Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language:
- nl
widget:
- source_sentence: "De kat slaapt op het bed."
sentences:
- "De poes rust op het matras."
- "De hond slaapt naast het bed."
- "Het bed is gemaakt van hout."
---
# allnli-GroNLP-bert-base-dutch-cased
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["De kat slaapt op het bed.", "De poes rust op het matras."]
model = SentenceTransformer('textgain/allnli-GroNLP-bert-base-dutch-cased')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ["De kat slaapt op het bed.", "De poes rust op het matras."]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('textgain/allnli-GroNLP-bert-base-dutch-cased')
model = AutoModel.from_pretrained('textgain/allnli-GroNLP-bert-base-dutch-cased')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 4388 with parameters:
```
{'batch_size': 128}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 438,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 439,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
drattung/ppo-Huggy
|
drattung
| 2023-08-06T18:08:18Z | 0 | 1 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-06T18:08:06Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: drattung/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ThuyNT03/xlm-roberta-base-finetuned-panx-de
|
ThuyNT03
| 2023-08-06T18:06:14Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-06T17:49:40Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8616659101225601
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1329
- F1: 0.8617
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2568 | 1.0 | 525 | 0.1583 | 0.8125 |
| 0.1261 | 2.0 | 1050 | 0.1458 | 0.8473 |
| 0.0823 | 3.0 | 1575 | 0.1329 | 0.8617 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
ishwarbb23/t52
|
ishwarbb23
| 2023-08-06T17:53:05Z | 101 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:ThomasSimonini/t5-end2end-question-generation",
"base_model:finetune:ThomasSimonini/t5-end2end-question-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-05T18:12:16Z |
---
license: apache-2.0
base_model: ThomasSimonini/t5-end2end-question-generation
tags:
- generated_from_trainer
model-index:
- name: t52
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t52
This model is a fine-tuned version of [ThomasSimonini/t5-end2end-question-generation](https://huggingface.co/ThomasSimonini/t5-end2end-question-generation) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6944
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2217 | 0.65 | 100 | 2.9125 |
| 2.9732 | 1.3 | 200 | 2.8349 |
| 2.8996 | 1.95 | 300 | 2.7879 |
| 2.8009 | 2.59 | 400 | 2.7614 |
| 2.7532 | 3.24 | 500 | 2.7406 |
| 2.6964 | 3.89 | 600 | 2.7208 |
| 2.6462 | 4.54 | 700 | 2.7153 |
| 2.6265 | 5.19 | 800 | 2.7037 |
| 2.6089 | 5.84 | 900 | 2.6968 |
| 2.5522 | 6.49 | 1000 | 2.6944 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
roa7n/gpt2-human_nontata_promoters-last_2_layer_randomized
|
roa7n
| 2023-08-06T17:39:18Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T17:39:16Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
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