---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3000
- loss:MultipleNegativesRankingLoss
- llama-cpp
- gguf-my-repo
base_model: trbeers/distilroberta-base-nli-v2
datasets:
- sentence-transformers/all-nli
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: An Indian woman is washing and cleaning dirty laundry at a lake
    and in the background is a kid who appears to have jumped into the lake.
  sentences:
  - An Indian woman is doing her laundry in a lake.
  - An Indian woman is putting her laundry into the machine.
  - A girl is playing with a Slinky.
- source_sentence: Nine women in white robes with hoods walk on plush, green grass.
  sentences:
  - The women each have one head.
  - Two friends sitting on step at their job.
  - The woman is alone and asleep in her bedroom.
- source_sentence: Under a blue sky with white clouds, a child reaches up to touch
    the propeller of a plane standing parked on a field of grass.
  sentences:
  - A child is reaching to touch the propeller of a plane.
  - The boy is sitting
  - A child is playing with a ball.
- source_sentence: A man and a woman are talking in a park
  sentences:
  - A man is heading to his house of worship.
  - A pair of people are talking outdoors.
  - A man and woman are talking in the aquarium.
- source_sentence: A man running a marathon talks to his friend.
  sentences:
  - People watching hot air balloons inflating.
  - There is a man running.
  - There are people canoeing down a river.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on distilbert/distilroberta-base
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.7444932434233196
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7769282355085634
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7502489213535852
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7574428535049513
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.752089041601621
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7583983155030144
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.49365896310259416
      name: Pearson Dot
    - type: spearman_dot
      value: 0.49513705166832495
      name: Spearman Dot
    - type: pearson_max
      value: 0.752089041601621
      name: Pearson Max
    - type: spearman_max
      value: 0.7769282355085634
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.7101248020205797
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7072744861979087
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7133109440593921
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6966728374126535
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7142547715068376
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6959833440145297
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.4503698330540162
      name: Pearson Dot
    - type: spearman_dot
      value: 0.43425556993054526
      name: Spearman Dot
    - type: pearson_max
      value: 0.7142547715068376
      name: Pearson Max
    - type: spearman_max
      value: 0.7072744861979087
      name: Spearman Max
---

# Sleem247/distilroberta-base-nli-v2-Q8_0-GGUF
This model was converted to GGUF format from [`trbeers/distilroberta-base-nli-v2`](https://huggingface.co/trbeers/distilroberta-base-nli-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/trbeers/distilroberta-base-nli-v2) for more details on the model.

## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Sleem247/distilroberta-base-nli-v2-Q8_0-GGUF --hf-file distilroberta-base-nli-v2-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Sleem247/distilroberta-base-nli-v2-Q8_0-GGUF --hf-file distilroberta-base-nli-v2-q8_0.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Sleem247/distilroberta-base-nli-v2-Q8_0-GGUF --hf-file distilroberta-base-nli-v2-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Sleem247/distilroberta-base-nli-v2-Q8_0-GGUF --hf-file distilroberta-base-nli-v2-q8_0.gguf -c 2048
```