slinger-single-word

SentenceTransformer based on intfloat/e5-base

This is a sentence-transformers model finetuned from intfloat/e5-base on the train dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: intfloat/e5-base <!-- at revision b533fe4636f4a2507c08ddab40644d20b0006d6a -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity
• Training Dataset:
  • train

<!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, '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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Essential Soft-skills & Competencies for Today’s Executives & Employees\nSoft-skills are defined as the personal attributes that enhance and individual’s interaction, job performance and career prospect. Soft-skills are interpersonal and generally applicable.\n- Excellent presentation skill.\n- Emotional intelligence.\n- Social/networking skill,\n- Business writing/business communication skills.\n- Effective communication skill (fluent speaking and good writing).\n- Work/workplace etiquette.\n- Team playing – the ability to work productively and effectively with others. Also, inter-personal skill.\n- Information and communication technology/computer skills.\n- Apt to learning – ability to learn and adapt to a job very fast with minimal supervision and explanation. Being clever and resourceful.\n- Teaching/training/mentoring skills and abilities.\n- Numerical/mathematical skill.\n- Information gathering and acquisition skills. Also, the ability to keep tabs of happenings in your business, corporate, socio-economic and political environment.\n- Anticipation skill – always try to see things/events coming before they actually arrived/happen.\n- Leadership, supervisory and managerial skills.\n- Adaptability to organisational/corporate changes (both role changes and management changes. Also, change management or transition management.\n- Initiative & independent skills/innovation & idea generation skills.\n- Financial resources management skill.\n- Human resources development and management skill.\n- Conflict resolution and conflict management skills.\n- Motivational skill – ability to motivate your team/group/workers. Also, self-motivation and management skills.\n- Flexibility and creativity. Also, creative thinking skill.\n- Risk taking, risk bearing and risk management skills.\n- Financial report/financial statement interpretation and analysing skills.\nNote that, today’s workplace requires that employees, managers and executives to possess these essential soft-skills…\nLike our page: facebook.com/vaxity',
    'Skills',
    'AMP',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

train

• Dataset: train
• Size: 100,000 training samples
• Columns: <code>anchor</code> and <code>positive</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	<ul><li>min: 51 tokens</li><li>mean: 386.54 tokens</li><li>max: 512 tokens</li></ul>	<ul><li>min: 3 tokens</li><li>mean: 3.46 tokens</li><li>max: 7 tokens</li></ul>

• Samples:

  anchor	positive
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