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Prashasst
/
anime-recommendation-model

Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:2353
loss:CosineSimilarityLoss
Eval Results
Inference Endpoints
Model card Files Community
Prashasst commited on
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Add new SentenceTransformer model

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Files changed (11) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +591 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +20 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +51 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +72 -0
  11. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ base_model: sentence-transformers/all-mpnet-base-v2
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+ library_name: sentence-transformers
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+ metrics:
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+ - pearson_cosine
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+ - spearman_cosine
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+ pipeline_tag: sentence-similarity
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+ tags:
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+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
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+ - generated_from_trainer
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+ - dataset_size:2353
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+ - loss:CosineSimilarityLoss
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+ widget:
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+ - source_sentence: A year has passed since "The Black Rebellion" and the remaining
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+ Black Knights have vanished into the shadows, their leader and figurehead, Zero,
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+ executed by the Britannian Empire. Area 11 is once more squirming under the Emperors
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+ oppressive heel as the Britannian armies concentrate their attacks on the European
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+ front. But for the Britannians living in Area 11, life is back to normal. On one
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+ such normal day, a Britannian student, skipping his classes in the Ashford Academy,
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+ sneaks out to gamble on his chess play. But unknown to this young man, several
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+ forces are eying him from the shadows, for soon, he will experience a shocking
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+ encounter with his own obscured past, and the masked rebel mastermind Zero will
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+ return.
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+ sentences:
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+ - Politics
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+ - Mythology
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+ - Disability
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+ - source_sentence: 'In a land where corruption rules and a ruthless Prime Minister
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+ has turned the puppet Emperors armies of soldiers, assassins and secret police
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+ against the people, only one force dares to stand against them: Night Raid, an
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+ elite team of relentless killers, each equipped with an Imperial Arm - legendary
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+ weapons with unique and incredible powers created in the distant past.'
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+ sentences:
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+ - Kuudere
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+ - Tragedy
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+ - Seinen
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+ - source_sentence: Theres a rumor about a mysterious phenomenon called "puberty syndrome."
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+ For example, Sakuta Azusagawa is a high school student who suddenly sees a bunny
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+ girl appear in front of him. The girl is actually a girl named Mai Sakurajima,
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+ who is Sakutas upperclassman who is also a famous actress who has gone on hiatus
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+ from the entertainment industry. For some reason, the people around Mai cannot
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+ see her bunny-girl figure. Sakuta sets out to solve this mystery, and as he spends
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+ time with Mai, he learns her secret feelings. Other heroines who have "puberty
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+ syndrome" start to appear in front of Sakuta.
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+ sentences:
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+ - Heterosexual
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+ - Drama
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+ - Episodic
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+ - source_sentence: Dororo, a young orphan thief, meets Hyakkimaru, a powerful ronin.
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+ Hyakkimarus father, a greedy feudal lord, had made a pact with 12 demons, offering
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+ his yet-unborn sons body parts in exchange for great power. Thus, Hyakkimaru -
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+ who was born without arms, legs, eyes, ears, a nose or a mouth - was abandoned
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+ in a river as a baby. Rescued and raised by Dr. Honma, who equips him with artificial
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+ limbs and teaches him sword-fighting techniques, Hyakkimaru discovers that each
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+ time he slays a demon, a piece of his body is restored. Now, he roams the war-torn
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+ countryside in search of demons.
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+ sentences:
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+ - Urban
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+ - Heterosexual
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+ - Demons
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+ - source_sentence: Everyone has a part of themselves they cannot show to anyone else.
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+ sentences:
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+ - Transgender
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+ - Crime
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+ - Comedy
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+ model-index:
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+ - name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
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+ results:
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: anime recommendation dev
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+ type: anime-recommendation-dev
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.6144532877889222
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.6215240802205049
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+ name: Spearman Cosine
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: anime recommendation test
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+ type: anime-recommendation-test
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.6535704432727567
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.6393952594394526
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+ name: Spearman Cosine
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+ ---
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+
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+ # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
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+ - **Maximum Sequence Length:** 384 tokens
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+ - **Output Dimensionality:** 768 dimensions
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+ - **Similarity Function:** Cosine Similarity
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+ <!-- - **Training Dataset:** Unknown -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
118
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
119
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
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+ ### Full Model Architecture
122
+
123
+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
126
+ (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})
127
+ (2): Normalize()
128
+ )
129
+ ```
130
+
131
+ ## Usage
132
+
133
+ ### Direct Usage (Sentence Transformers)
134
+
135
+ First install the Sentence Transformers library:
136
+
137
+ ```bash
138
+ pip install -U sentence-transformers
139
+ ```
140
+
141
+ Then you can load this model and run inference.
142
+ ```python
143
+ from sentence_transformers import SentenceTransformer
144
+
145
+ # Download from the 🤗 Hub
146
+ model = SentenceTransformer("Prashasst/anime-recommendation-model")
147
+ # Run inference
148
+ sentences = [
149
+ 'Everyone has a part of themselves they cannot show to anyone else.',
150
+ 'Crime',
151
+ 'Comedy',
152
+ ]
153
+ embeddings = model.encode(sentences)
154
+ print(embeddings.shape)
155
+ # [3, 768]
156
+
157
+ # Get the similarity scores for the embeddings
158
+ similarities = model.similarity(embeddings, embeddings)
159
+ print(similarities.shape)
160
+ # [3, 3]
161
+ ```
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+
163
+ <!--
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+ ### Direct Usage (Transformers)
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+
166
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
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+ </details>
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+ -->
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+
171
+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
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+ You can finetune this model on your own dataset.
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+
176
+ <details><summary>Click to expand</summary>
177
+
178
+ </details>
179
+ -->
180
+
181
+ <!--
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+ ### Out-of-Scope Use
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+
184
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
185
+ -->
186
+
187
+ ## Evaluation
188
+
189
+ ### Metrics
190
+
191
+ #### Semantic Similarity
192
+
193
+ * Datasets: `anime-recommendation-dev` and `anime-recommendation-test`
194
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
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+ | Metric | anime-recommendation-dev | anime-recommendation-test |
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+ |:--------------------|:-------------------------|:--------------------------|
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+ | pearson_cosine | 0.6145 | 0.6536 |
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+ | **spearman_cosine** | **0.6215** | **0.6394** |
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Dataset
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+
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+ #### Unnamed Dataset
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+
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+
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+ * Size: 2,353 training samples
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+ * Columns: <code>description</code>, <code>genre</code>, and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | description | genre | label |
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+ |:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 15 tokens</li><li>mean: 97.39 tokens</li><li>max: 193 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.82 tokens</li><li>max: 8 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.71</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | description | genre | label |
229
+ |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------|:------------------|
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+ | <code>Mitsuha Miyamizu, a high school girl, yearns to live the life of a boy in the bustling city of Tokyo—a dream that stands in stark contrast to her present life in the countryside. Meanwhile in the city, Taki Tachibana lives a busy life as a high school student while juggling his part-time job and hopes for a future in architecture.</code> | <code>Environmental</code> | <code>0.6</code> |
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+ | <code>Jinta Yadomi and his group of childhood friends have become estranged after a tragic accident split them apart. Now in their high school years, a sudden surprise forces each of them to confront their guilt over what happened that day and come to terms with the ghosts of their past.</code> | <code>Hikikomori</code> | <code>0.79</code> |
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+ | <code>The second season of <i>Ansatsu Kyoushitsu</i>.</code> | <code>Episodic</code> | <code>0.44</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
234
+ ```json
235
+ {
236
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
237
+ }
238
+ ```
239
+
240
+ ### Evaluation Dataset
241
+
242
+ #### Unnamed Dataset
243
+
244
+
245
+ * Size: 294 evaluation samples
246
+ * Columns: <code>description</code>, <code>genre</code>, and <code>label</code>
247
+ * Approximate statistics based on the first 294 samples:
248
+ | | description | genre | label |
249
+ |:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------|
250
+ | type | string | string | float |
251
+ | details | <ul><li>min: 15 tokens</li><li>mean: 92.48 tokens</li><li>max: 193 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.73 tokens</li><li>max: 8 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.69</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | description | genre | label |
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+ |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------|:------------------|
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+ | <code>Summer is here, and the heroes of Class 1-A and 1-B are in for the toughest training camp of their lives A group of seasoned pros pushes everyones Quirks to new heights as the students face one overwhelming challenge after another. Braving the elements in this secret location becomes the least of their worries when routine training turns into a critical struggle for survival.</code> | <code>Transgender</code> | <code>0.2</code> |
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+ | <code>"In order for something to be obtained, something of equal value must be lost."</code> | <code>Cyborg</code> | <code>0.72</code> |
257
+ | <code>In the story, Subaru Natsuki is an ordinary high school student who is lost in an alternate world, where he is rescued by a beautiful, silver-haired girl. He stays near her to return the favor, but the destiny she is burdened with is more than Subaru can imagine. Enemies attack one by one, and both of them are killed. He then finds out he has the power to rewind death, back to the time he first came to this world. But only he remembers what has happened since.</code> | <code>Primarily Female Cast</code> | <code>0.61</code> |
258
+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
259
+ ```json
260
+ {
261
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
262
+ }
263
+ ```
264
+
265
+ ### Training Hyperparameters
266
+ #### Non-Default Hyperparameters
267
+
268
+ - `eval_strategy`: steps
269
+ - `per_device_train_batch_size`: 16
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+ - `learning_rate`: 2e-05
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+ - `num_train_epochs`: 1
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+ - `warmup_ratio`: 0.1
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+ - `fp16`: True
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+
275
+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
277
+
278
+ - `overwrite_output_dir`: False
279
+ - `do_predict`: False
280
+ - `eval_strategy`: steps
281
+ - `prediction_loss_only`: True
282
+ - `per_device_train_batch_size`: 16
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+ - `per_device_eval_batch_size`: 8
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+ - `per_gpu_train_batch_size`: None
285
+ - `per_gpu_eval_batch_size`: None
286
+ - `gradient_accumulation_steps`: 1
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+ - `eval_accumulation_steps`: None
288
+ - `torch_empty_cache_steps`: None
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+ - `learning_rate`: 2e-05
290
+ - `weight_decay`: 0.0
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+ - `adam_beta1`: 0.9
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+ - `adam_beta2`: 0.999
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+ - `adam_epsilon`: 1e-08
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+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 1
296
+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
298
+ - `lr_scheduler_kwargs`: {}
299
+ - `warmup_ratio`: 0.1
300
+ - `warmup_steps`: 0
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+ - `log_level`: passive
302
+ - `log_level_replica`: warning
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+ - `log_on_each_node`: True
304
+ - `logging_nan_inf_filter`: True
305
+ - `save_safetensors`: True
306
+ - `save_on_each_node`: False
307
+ - `save_only_model`: False
308
+ - `restore_callback_states_from_checkpoint`: False
309
+ - `no_cuda`: False
310
+ - `use_cpu`: False
311
+ - `use_mps_device`: False
312
+ - `seed`: 42
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+ - `data_seed`: None
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+ - `jit_mode_eval`: False
315
+ - `use_ipex`: False
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+ - `bf16`: False
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+ - `fp16`: True
318
+ - `fp16_opt_level`: O1
319
+ - `half_precision_backend`: auto
320
+ - `bf16_full_eval`: False
321
+ - `fp16_full_eval`: False
322
+ - `tf32`: None
323
+ - `local_rank`: 0
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+ - `ddp_backend`: None
325
+ - `tpu_num_cores`: None
326
+ - `tpu_metrics_debug`: False
327
+ - `debug`: []
328
+ - `dataloader_drop_last`: False
329
+ - `dataloader_num_workers`: 0
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+ - `dataloader_prefetch_factor`: None
331
+ - `past_index`: -1
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+ - `disable_tqdm`: False
333
+ - `remove_unused_columns`: True
334
+ - `label_names`: None
335
+ - `load_best_model_at_end`: False
336
+ - `ignore_data_skip`: False
337
+ - `fsdp`: []
338
+ - `fsdp_min_num_params`: 0
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+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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+ - `fsdp_transformer_layer_cls_to_wrap`: None
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+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
342
+ - `deepspeed`: None
343
+ - `label_smoothing_factor`: 0.0
344
+ - `optim`: adamw_torch
345
+ - `optim_args`: None
346
+ - `adafactor`: False
347
+ - `group_by_length`: False
348
+ - `length_column_name`: length
349
+ - `ddp_find_unused_parameters`: None
350
+ - `ddp_bucket_cap_mb`: None
351
+ - `ddp_broadcast_buffers`: False
352
+ - `dataloader_pin_memory`: True
353
+ - `dataloader_persistent_workers`: False
354
+ - `skip_memory_metrics`: True
355
+ - `use_legacy_prediction_loop`: False
356
+ - `push_to_hub`: False
357
+ - `resume_from_checkpoint`: None
358
+ - `hub_model_id`: None
359
+ - `hub_strategy`: every_save
360
+ - `hub_private_repo`: False
361
+ - `hub_always_push`: False
362
+ - `gradient_checkpointing`: False
363
+ - `gradient_checkpointing_kwargs`: None
364
+ - `include_inputs_for_metrics`: False
365
+ - `eval_do_concat_batches`: True
366
+ - `fp16_backend`: auto
367
+ - `push_to_hub_model_id`: None
368
+ - `push_to_hub_organization`: None
369
+ - `mp_parameters`:
370
+ - `auto_find_batch_size`: False
371
+ - `full_determinism`: False
372
+ - `torchdynamo`: None
373
+ - `ray_scope`: last
374
+ - `ddp_timeout`: 1800
375
+ - `torch_compile`: False
376
+ - `torch_compile_backend`: None
377
+ - `torch_compile_mode`: None
378
+ - `dispatch_batches`: None
379
+ - `split_batches`: None
380
+ - `include_tokens_per_second`: False
381
+ - `include_num_input_tokens_seen`: False
382
+ - `neftune_noise_alpha`: None
383
+ - `optim_target_modules`: None
384
+ - `batch_eval_metrics`: False
385
+ - `eval_on_start`: False
386
+ - `eval_use_gather_object`: False
387
+ - `prompts`: None
388
+ - `batch_sampler`: batch_sampler
389
+ - `multi_dataset_batch_sampler`: proportional
390
+
391
+ </details>
392
+
393
+ ### Training Logs
394
+ <details><summary>Click to expand</summary>
395
+
396
+ | Epoch | Step | Training Loss | Validation Loss | anime-recommendation-dev_spearman_cosine | anime-recommendation-test_spearman_cosine |
397
+ |:------:|:----:|:-------------:|:---------------:|:----------------------------------------:|:-----------------------------------------:|
398
+ | 0.0068 | 1 | 0.3882 | - | - | - |
399
+ | 0.0135 | 2 | 0.2697 | - | - | - |
400
+ | 0.0203 | 3 | 0.2648 | - | - | - |
401
+ | 0.0270 | 4 | 0.3022 | - | - | - |
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+ | 0.0338 | 5 | 0.2665 | - | - | - |
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+ | 0.0405 | 6 | 0.2923 | - | - | - |
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+ | 0.0473 | 7 | 0.3165 | - | - | - |
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+ | 0.0541 | 8 | 0.2069 | - | - | - |
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+ | 0.0608 | 9 | 0.271 | - | - | - |
407
+ | 0.0676 | 10 | 0.1974 | - | - | - |
408
+ | 0.0743 | 11 | 0.156 | - | - | - |
409
+ | 0.0811 | 12 | 0.1035 | - | - | - |
410
+ | 0.0878 | 13 | 0.1046 | - | - | - |
411
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545
+ | 1.0 | 148 | 0.0028 | - | - | 0.6394 |
546
+
547
+ </details>
548
+
549
+ ### Framework Versions
550
+ - Python: 3.10.12
551
+ - Sentence Transformers: 3.3.1
552
+ - Transformers: 4.44.2
553
+ - PyTorch: 2.4.1+cu121
554
+ - Accelerate: 0.34.2
555
+ - Datasets: 3.2.0
556
+ - Tokenizers: 0.19.1
557
+
558
+ ## Citation
559
+
560
+ ### BibTeX
561
+
562
+ #### Sentence Transformers
563
+ ```bibtex
564
+ @inproceedings{reimers-2019-sentence-bert,
565
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
566
+ author = "Reimers, Nils and Gurevych, Iryna",
567
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
568
+ month = "11",
569
+ year = "2019",
570
+ publisher = "Association for Computational Linguistics",
571
+ url = "https://arxiv.org/abs/1908.10084",
572
+ }
573
+ ```
574
+
575
+ <!--
576
+ ## Glossary
577
+
578
+ *Clearly define terms in order to be accessible across audiences.*
579
+ -->
580
+
581
+ <!--
582
+ ## Model Card Authors
583
+
584
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
585
+ -->
586
+
587
+ <!--
588
+ ## Model Card Contact
589
+
590
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
591
+ -->
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